Анализ на изображения в експерименталната токсикология

Йорданов, Йордан

doi:10.5281/zenodo.10866504

Published March 24, 2024 | Version v1

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Анализ на изображения в експерименталната токсикология

Йорданов, Йордан (Other)¹

1. Катедра по фармакология, фармакотерапия и токсикология, Фармацевтичен факултет, Медицински университет - София

Чл.-кор. проф. Илза Константинова Пъжева, дбн
Секция QSAR молекулно моделиране,
Институт по биофизика и биомедицинско
инженерство,
Българска академия на науките

РЕЦЕНЗИЯ

на монографията „Анализ на изображения в експерименталната
токсикология“
от гл. ас. Йордан Иванов Йорданов, дф

Монографичният труд на гл.ас. доктор Йордан Йорданов на тема „Анализ на изображения в експерименталната токсикология“ се появява в период от развитието на науката и технологиите, в който въпросите за коректната обработка на големи масиви от данни, съдържателната им интерпретация и поставянето на получените знания в служба на научния прогрес и нуждите на обществото, е изключително актуален.

Трудът представя темата и структурата на изследването по разбираем и логичен начин като оптимално комбинира теоретични знания и практически наблюдения. Той започва с анализ на специфичните особености, които имат цифровите изображения и по-специално биоизображенията (отнасящи се
до биологични обекти). Анализът обосновава нуждата от интелигентен подход в обработката им, за да бъде извлечена най-съдържателната информация. Това въведение естествено довежда до формулиране на целта на монографията, която найобщо би могла да се определи така – да предостави базови теоретични знания и практически умения по приложението на методите за анализ на цифрови изображения в неклиничните токсикологични проучвания. В съответствие с целта следва представяне на теоретичните основи с акцент върху свойствата на цифровите изображения, видовете операции и методите, прилагани при обработката им. Систематичният обзор последователно извежда селекцията на най-значимите публикации по темата с акцент върху анализ на биоизображения
в областта на експерименталната токсикология. Представени са и основни подходи при анализ на биоизображения по собствени резултати на автора в панел от различни токсикологични изследвания.

Целевата група читатели на монографичния труд са всички изследователи, които се интересуват от обработка на биоизображения. Те могат да бъдат напълно удовлетворени от съдържанието на монографията, тъй като в нея са събрани и обобщени наличните в момента методи, софтуерни продукти със свободен и безплатен достъп, обучителни ресурси и техники, които текущо са разпръснати в различни източници или фрагментирано представени в книги, дисертации, публикации и интернет източници. Считам, че монографията запълва една съществена празнина в нашия научен книжен/електронен пазар и по такъв начин би насърчила употребата на методите за анализ на изображения сред по-широк кръг от изследователи и особено сред онези от тях, които работят в биомедицинската сфера. Следва да се подчертае силно интердисциплинарният характер на труда, който обединява познания в няколко научни области (физика, математика, биология, медицина, информатика и др.). В същото време той се явява своебразен хибрид между базови теоретични познания и ценни практически съвети, което го прави особено полезен за работещите в полето на биологията и медицината и които имат интерес към темата по анализ на изображения.

По-долу се спирам по-подробно върху някои от основните точки в изложението на представения монографичен труд. Авторът последователно описва основните стъпки при анализ на изображения (вкл. биоизображения), по-конкретно: генерирането на изображения, предварителната им обработка, същинския анализ, извличането на информация в подходящ формат, интерпретацията на получената информация и формулирането на изводи за релевантните характеристики на обектите в изображението. Всяка от тези стъпки е анализирана като са очертани основните операции и алгоритми за съответната стъпка. Усвоени в дълбочина и описани са голям брой софтуерни разработки за анализ на изображения, които са критично анализирани от гл. т. на тяхната приложимост при обработка на биоизображения. За да постигне това, авторът е изследвал използването им и по такъв начин е придобил ценен практически опит, който обаче не би бил постижим без разбирането на същностните алгоритми и процеси, които са имплементирани в съответните софтуерни продукти и които той адресира в изложението си.

Отделено е специално внимание и на много актуалния напоследък „изкуствен интелект“ (artificial intelligence, AI), проследявайки неговите възможности и ограничения в специфичната за анализ на изображения област. Представени са дълбоките невронни мрежи като инструмент на AI и един от найуспешните методи за обработка на големи масиви от данни, характерни за биоизображенията. Прави впечатление критичният поглед на автора по отношение на обучителните методи и невъзможността им да прогнозират/ класифицират обекти, ако те не са били представени в обучителната група данни на модела, както и трудностите в интерпретацията на изходните резултати, поради липсата на наблюдение върху процеса на обучение. За яснота, той „екстраполира“ тезипроблеми върху биологични обекти, с което адаптира изложението към изследваната тема.

Особено ценна част в монографията е систематичният обзор, който насочва описаните преди него теоретични методологични основи към придобиването на обективна представа за приложенията на методите за анализ на биоизображения в експерименталните токсикологични изследвания. Последните се отнасят до тясната експертиза на автора и са крайната му цел в обработката и анализа на биоизображения. В обзора той се базира на обективна, научно проверена информация, достъпна чрез Core Collection на библиографската информационна система Web of Science (WoS). Авторът се възползва от възможностите, които дава WoS, за да реализира няколко нива на селекция на публикациите, позволяващи му да извлече най-съотносимата към интересуващия го обект научна библиография. Забележителна е селекцията, която тръгва от почти 2 млн. заглавия и се свежда след серията от филтри до около 700 и след допълнителен
анализ до около 80 най-значими публикации по експериментална токсикология, интегрираща анализи на изображения. Извършен е задълбочен анализ на тези
публикации по отношение на обектите и методите на изследване на токсичност и са очертани тези от тях, които определят найсъвременните тенденции в полето. Проведена е класификация по няколко критерия като вид изследвани вещества, токсикологични дисциплини, експериментална моделна система, вид биомаркер, техника за заснемане и др. Като цяло систематичният обзор е едно оригинално конструирано и аналитично изследване, което заслужава да бъде публикувано
като самостоятелна статия, за да стане достояние на по-широка читателска аудитория.

В монографията е включена и глава, насочваща към собствените изследвания на автора, свързана с разработването на подходи за анализ на биоизображения. Изложението е логично конструирано като започва от подбора на подходящ софтуер, анализиран от гл. т. на неговата достъпност, функционална структура, адекватност за изследвания обект, наличен интерфейс, програмни езици и библиотеки, управление на инфраструктура и др. Самостоятелно е разгледан софтуер за дълбоко обучение в съответствие с най-съвременните тенденции на използване на AI. Списъкът от анализирани софтуери е наистина внушителен и разкрива задълбоченото изучаване от автора на техните качества и ограничения. Цитирани са добри практики и е направен анализ на често срещани грешки, очертани са основните тенденции и перспективи в развитието на специализиран за обработка на изображения софтуер, препоръки за избора му, както и възможността за използване на AI-базирани текстови редактори (чатботове) за избор на най-подходящ софтуер. В същия раздел е представена батерия от методи за анализ на изображения в експерименталната токсикология като собствени разработки на автора по отношение на: (а) определяне на конфлуентност на монослой в двумерни клетъчни култури, (б) идентифициране на окръглени клетки с апоптотична морфология, (в) тест за миграция чрез надраскване на монослой, (г) кометен тест, (д) анализ на интензитет и тъканно разпределение на имунохистохимични маркери; (е) анализ на навлизането на флуоресцентни молекули в резистентни туморни клетки. Всичките шест разработки са в различни апликации на анализ и обработка на биоизображения. Те илюстрират как разработените подходи, базирани на изложените в монографията теоретични основи и добри практики, разкриват възможност за извличане на полезна и съдържателна информация от изследваните биоизображения. Част от тези изследвания са публикувани, за други това предстои.

Достойнство на труда е наличието на възможност за свободен достъп на описаните изследвания чрез QR код доподдържана от автора репозитория, създадена на GitHub.com.

Монографията е написана на ясен, професионално издържан език и обхваща най-съвременните публикации в областта. Обемът й е 264 страници и включва 50 фигури, 5 таблици и 405 литературни източника.

В заключение, оценявам високо цялостната научна и практическа стойност на монографията и считам, че тя представлява принос към българската научна школа в областта, а авторът й оценявам като пионер сред анализаторите на биоизображения у нас. В раздела на систематичния обзор, касаещ географското разпределение на публикациите по темата на анализ на изображения с токсикологична тематика, прави впечатление отсъствието на страната ни от списъка на страните с обща публикационна активност. Вярвам, че настоящата монография ще стимулира работещите у нас в областта на експерименталната токсикология, интегрираща методи за анализ на изображения, и ще доведе до появата и трайното присъствие на страната ни на световната карта на изследователите в тази област.

Чл.-кор. проф. Илза Константинова Пъжева, дбн

Other

Проф. Георги Цв. Момеков, дфн
Катедра по фармакология, фармакотерапия и
токсикология,
Фармацевтичен факултет,
Медицински университет - София

РЕЦЕНЗИЯ

на монографията „Анализ на изображения в експерименталната
токсикология“
от гл. ас. Йордан Иванов Йорданов, дф

Предоставената за рецензия монография е един съвременен и задълбочен прочит на теоретичните основи, методичните подходи, аналитичния и дигитален инструментариум, и на приложните аспекти на методите за анализ на цифрови изображения с транслация в неклиничните токсикологични проучвания. В монографията наред с това е даден адекватен топикален преглед на достъпните технологии и платформи, в това число на софтуерни пакети със свободен достъп, както и на налични обучителни ресурси.

Микроскопските техники се развиха изключително интензивно през последните десетилетия, което доведе до експоненциално нарастване на броя, сложността и размера на генерираните при биомедицински студии дигитални биологични изображения. Анализът на изображения е научна област, възникнала през 60-те години и отдавна е еволюирала в континуум от инструментални методи и софтуерни продукти с огромен потенциал за реализации в областта на токсикологията. Несъмнено мощен тласък на тези технологии даде имплементирането на технологиите, базирани на изкуствен интелект, които несъмнено ще оставят неизменим отпечатък върху стратегиите и практическото реализиране на съвременната наука.

Въпреки че анализът на изображения бързо набира популярност в биомедицинските области, все още има пречки пред широкото приложение на тези методи. Това е така, защото въпреки че от десетилетия са разработвани отлични и богати софтуерни инструменти с отворен код за анализ на изображения в момента голям брой биомедицински учени изглежда са обезсърчени от дискомфорта от концептуалното разбиране и боравене с непознати математически или статистически операции и необходимостта да инвестират значително време в обучение или самообучение. С появата на широкомащабни езикови модели и наличието на платформи и модели за дълбоко обучение се появяват нови тенденции и знанието какво предлагат подходите за анализ на изображения ще помогне на изследователите да се възползват максимално от тях чрез прилагане на експериментални методи, свързани с тяхното генериране и обработка. Едновременно с това, цените на необходимия хардуер продължават да падат. Това очертава актуалността на тематиката на представената за рецензиране монография.

В книгата са разгледани в достатъчна дълбочина теоретичните
основи на снемането на дигитализирани изображения, подходите за тяхната обработка с извеждане на количествени или полуколичествени параметри, позволяващи използването на тази изследователска модалност като интегрален елемент от съвременните проучвани в областта на биомедицинските науки и в частност неклиничната токсикология. Авторът умело борави с комплексната терминология и е съумял да компилира
пропедевтична информация, която би била полезна за широка аудитория, в т.ч. на широк спектър от специалисти, които имат интерес към аналитична обработка на изображения при провеждане на експериментални проучвания. Монографията е написана на 225 страници (без съдържанието и
библиографията), като включва 50 фигури и 5 таблици. Цитирани са общо 405 литературни източника.

Особено достойнство на работата е, че е фокусирана както върху глобалните характеристики на технологиите за снемане на изображения и тяхното обработване и дигитализиране, така и на специфичните особености, свързани с валидирането на методологията и проблемите на практическата им транслация.

Монографията съдържа и систематичен обзор с надлежно представена методология и протокол за подбор и анализ на литературните данни и преглед със систематичен анализ на ключови транслиращи технологията проучвания. На практика в труда като интегрална част е включен първият по рода си систематичен обзор по темата като е предоставен и свободнодостъпен линк към авторски код за алгоритмите на част от представените анализи. Включена е практически-насочена част, съдържаща собствени експериментални приноси, които са
дисеминирани под формата на три оригинални пълнотекстови публикации в списания с IF, както и все още непубликувани проучвания, базирани на тази методология.

Убедено считам, че тази монография окупира важна ниша в съвременната българска научна литература и може да бъде препоръчана като компендиум на приложението на подходите за анализ на дигитализирани изображения в токсикологията. Това е една интересна, интелигентно написана монография,която експлицитно и в необстоятелствена форма представя както теоретичните особености така и възможностите за практическа реализация и ще бъде интересна за широка публика от специалисти.

Проф. Георги Цв. Момеков, дфн

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Additional details

Subtitle (Bulgarian): монография
Translated title (English): Image analysis in experimental toxicology
Subtitle (English): a monograph

Issued: 2024

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1. COBA (The Center for Open Bioimage Analysis). https://openbioimageanalysis.org/ https://openbioimageanalysis.org/. 2. NEUBIAS: Network of European BioImage Analysts - NEUBIAS: Network of BioImage Analysts. https://eubias.org/NEUBIAS/. 3. Rempfler, M. et al. Tracing cell lineages in videos of lens-free microscopy. Med. Image Anal. 48, 147–161 (2018). 4. Ma, L. et al. Metabolomics and mass spectrometry imaging reveal the chronic toxicity of indoxacarb to adult zebrafish (Danio rerio) livers. J. Hazard. Mater. 453, 131304 (2023). 5. Smith, A. R. A Pixel Is Not A Little Square, A Pixel Is Not A Little Square, A Pixel Is Not A Little Square! Microsoft Comput. Graph. Tech. Memo 6, (1995). 6. Eliceiri, K. W., Rueden, C., Mohler, W. A., Hibbard, W. L. & White, J. G. Analysis of Multidimensional Biological Image Data. BioTechniques 33, 1268–1273 (2002). 7. Viljoen, A. et al. Force spectroscopy of single cells using atomic force microscopy. Nat. Rev. Methods Primer 1, 1–24 (2021). 8. Bergenstråhle, J., Larsson, L. & Lundeberg, J. Seamless integration of image and molecular analysis for spatial transcriptomics workflows. BMC Genomics 21, 482 (2020). 9. Dries, R. et al. Advances in spatial transcriptomic data analysis. Genome Res. 31, 1706–1718 (2021). 10. Benoit, A., Caplier, A., Durette, B. & Herault, J. Using Human Visual System modeling for bio-inspired low level image processing. Comput. Vis. Image Underst. 114, 758–773 (2010). 11. Roberts, B., Harris, M. G. & Yates, T. A. The roles of inducer size and distance in the Ebbinghaus illusion (Titchener circles). Perception 34, 847–856 (2005). 12. Renier, L. et al. The Ponzo illusion with auditory substitution of vision in sighted and early-blind subjects. Perception 34, 857–867 (2005). 13. Troscianko, J. & Osorio, D. A model of colour appearance based on efficient coding of natural images. PLOS Comput. Biol. 19, e1011117 (2023). 14. Szeliski, R. Computer Vision: Algorithms and Applications. (Springer Nature, 2022). 15. Burger, W. & Burge, M. J. Digital Image Processing: An Algorithmic Introduction. (Springer Nature, 2022). 16. Bankhead, P. Introduction to Bioimage Analysis. (2023). 17. Miura, K. & Sladoje, N. Bioimage Data Analysis Workflows. (Springer Nature, 2019). 18. MinnaLearn & University of Helsinki. Elements of AI course: Introduction to AI. (2018). 19. Пенчо Г. Венков. Анализ и Разпознаване На Изображения и Сцени. (ТУ София, 1996). 20. Милан П. Петров, Иван Г. Димитров, & Димитър Н. Балтаджиев. Въведение в Компютърната Обработка На Медицински Изображения. (Унив. изд. Св. Климент Охридски;София, 1998). 21. Огнян И. Железов & Лъчезар М. Георгиев. Компютърна Обработка На Сигнали и Изображения. (ТУ Варна, 1999). 22. Борислав Маринов. Цифрова Обработка На Изображения. (УАСГ, 2014). 23. Чавез, К. & Фокнър, А. Adobe Photoshop CC: Официален Курс На Adobe Systems. (Алекс Софт, 2022). 24. Йосифов, Т. Астрофотография с Цифров Фотоапарат. (2012). 25. Гълъбов, М. Съвременни Технологии За Обработка и Визуализация На 3D Изображения. (2014). doi:10.13140/RG.2.1.2317.8088. 26. Гълъбов, М. Въведение в Компютърното Зрение. (2015). doi:10.13140/RG.2.1.2168.3043. 27. Петрова, Т. Изследване и Синтез На Алгоритми За Обработка На Радиолокационни и Оптични Изображения с Повишено Качество (Study and Synthesis of Algorithms for Aerial and Radar Image Processing with Enhanced Quality). (2019). 28. Оджаков, Ф. Методи За Определяне На Дентална Възраст - От Традицията Към Изкуствения Интелект : Монография. (София : [Симолини 94], 2023). 29. А. Христов, М. Нишева, & Д. Димов. Въведение в конволюционните невронни мрежи. Автоматика И Информатика 27–38 (2018). 30. Момчил Панайотов, Велислава Шишкова, & Александър Вътов. Датиране на икони чрез анализ на изображения на дървени елементи заснети с цифров фотоапарат. АРХЕОМЕТРИЯ Archaeom. 9, 213–227 (2019). 31. Борислав Маринов. Приложение на цифровата обработка на изображения за автоматизиране на технологични процеси във фотограметрията. (УАСГ, 1999). 32. Георги К. Петров. Многомерна цифрова обработка и анализ на последователности от изображения. (2008). 33. Иванова, К. Нов метод за анализ на съдържанието на изображения от картинни колекции, използващ семантиката на цветовете. (2011). 34. Иванчо Т. Кънев. FPGA – базирани линейни пространствени филтри. (2017). 35. Антония Д. Михайлова. Алгоритми за автоматизирано сегментиране при голям брой медицински изображения. (Технически университет - София, 2018). 36. Gonzalez, R. C., Woods, R. E. & Eddins, S. L. Digital Image Processing Using MATLAB, 2nd Ed. by Rafael C. Gonzalez. (Gatesmark Publishing, S.I., 2009). 37. Serra, J. & Soille, P. Mathematical Morphology and Its Applications to Image Processing. (Springer Science & Business Media, 2012). 38. Chaki, J. & Dey, N. A Beginner's Guide to Image Preprocessing Techniques. (CRC Press, 2018). 39. Chaki, J. & Dey, N. A Beginner's Guide to Image Shape Feature Extraction Techniques. (CRC Press, 2019). 40. Zhang, Y.-J. A Selection of Image Analysis Techniques: From Fundamental to Research Front. (CRC Press, 2022). 41. Frery, A. C., Wu, J. & Gomez, L. SAR Image Analysis - A Computational Statistics Approach: With R Code, Data, and Applications. (Wiley, 2022). 42. Vollmer, S. et al. Online learning Tool for Research Integrity and image processing. https://ori.hhs.gov/education/products/RIandImages/default.html (2008). 43. Paulsen, R. R. & Moeslund, T. B. Introduction to Medical Image Analysis. (Springer Nature, 2020). 44. Koundal, D. & Gupta, S. Advances in Computational Techniques for Biomedical Image Analysis: Methods and Applications. (Elsevier Science, 2020). 45. Chen, M. Computer Vision for Microscopy Image Analysis. (Academic Press, 2020). 46. Pawley, J. B. Points, Pixels, and Gray Levels: Digitizing Image Data. in Handbook Of Biological Confocal Microscopy (ed. Pawley, J. B.) 59–79 (Springer US, Boston, MA, 2006). doi:10.1007/978-0-387-45524-2_4. 47. Shannon, C. E. Communication in the Presence of Noise. Proc. IRE 37, 10–21 (1949). 48. Nyquist, H. Certain Topics in Telegraph Transmission Theory. Trans. Am. Inst. Electr. Eng. 47, 617–644 (1928). 49. Whittaker, E. T. XVIII.—On the Functions which are represented by the Expansions of the Interpolation-Theory. Proc. R. Soc. Edinb. 35, 181–194 (1915). 50. Nyquist Conditions. https://imb.uq.edu.au/research/facilities/microscopy/training-manuals/microscopy-online-resources/image-capture/nyquist-conditions (2020). 51. Pawley, J. Handbook of biological confocal microscopy. (2006). 52. Por, E., van Kooten, M. & Sarkovic, V. Nyquist–Shannon sampling theorem. Leiden Univ. 1, (2019). 53. Jaques, C. & Liebling, M. Aliasing mitigation in optical microscopy of dynamic biological samples by use of temporally modulated color illumination and a standard RGB camera. J. Biomed. Opt. 25, (2020). 54. Sellaro, T. L. et al. Relationship between magnification and resolution in digital pathology systems. J. Pathol. Inform. 4, 21 (2013). 55. Heilemann, M. Fluorescence microscopy beyond the diffraction limit. J. Biotechnol. 149, 243–251 (2010). 56. Zhang, B., Zerubia, J. & Olivo-Marin, J.-C. Gaussian approximations of fluorescence microscope point-spread function models. Appl. Opt. 46, 1819–1829 (2007). 57. Wiklund, M., Brismar, H. & Önfelt, B. Acoustofluidics 18: Microscopy for acoustofluidic micro-devices. Lab. Chip 12, 3221–34 (2012). 58. Department, D. C. P. & OpenStax. 12.6 Limits of Resolution: The Rayleigh Criterion. (2016). 59. Rayleigh Length (Range) Calculator. https://www.gophotonics.com/calculators/rayleigh-length-range-calculator. 60. Rivera-Ortega, U. & Pico-Gonzalez, B. Wavelength estimation by using the Airy disk from a diffraction pattern with didactic purposes. Phys. Educ. 51, 015012 (2015). 61. Demmerle, J., Wegel, E., Schermelleh, L. & Dobbie, I. M. Assessing resolution in super-resolution imaging. Methods 88, 3–10 (2015). 62. Resolution. Nikon's MicroscopyU https://www.microscopyu.com/microscopy-basics/resolution. 63. Ross, S. T., Allen, J. R. & Davidson, M. W. Chapter 2 - Practical considerations of objective lenses for application in cell biology. in Methods in Cell Biology (eds. Waters, J. C. & Wittman, T.) vol. 123 19–34 (Academic Press, 2014). 64. idan. How oil immersion objectives can improve your microscopy? IDEA Bio-Medical http://idea-bio.com/how-oil-immersion-objectives-can-improve-your-microscopy/ (2021). 65. Ram, S., Ward, E. S. & Ober, R. J. Beyond Rayleigh's criterion: A resolution measure with application to single-molecule microscopy. Proc. Natl. Acad. Sci. 103, 4457–4462 (2006). 66. Jonkman, J., Brown, C. M., Wright, G. D., Anderson, K. I. & North, A. J. Tutorial: guidance for quantitative confocal microscopy. Nat. Protoc. 15, 1585–1611 (2020). 67. Elliott, A. D. Confocal Microscopy: Principles and Modern Practices. Curr. Protoc. Cytom. 92, e68 (2020). 68. White, J. G., Squirrell, J. M. & Eliceiri, K. W. Applying Multiphoton Imaging to the Study of Membrane Dynamics in Living Cells. Traffic Cph. Den. 2, 775–780 (2001). 69. Lanni, F., Waggoner, A. S. & Taylor, D. L. Structural organization of interphase 3T3 fibroblasts studied by total internal reflection fluorescence microscopy. J. Cell Biol. 100, 1091–1102 (1985). 70. Fish, K. N. Total Internal Reflection Fluorescence (TIRF) Microscopy. Curr. Protoc. Cytom. Editor. Board J Paul Robinson Manag. Ed. Al 0 12, Unit12.18 (2009). 71. Liu, C. et al. Super-resolution nanoscopy by coherent control on nanoparticle emission. Sci. Adv. 6, eaaw6579 (2020). 72. Hein, B., Willig, K. I. & Hell, S. W. Stimulated emission depletion (STED) nanoscopy of a fluorescent protein-labeled organelle inside a living cell. Proc. Natl. Acad. Sci. 105, 14271–14276 (2008). 73. Yeh, L.-H., Tian, L. & Waller, L. Structured illumination microscopy with unknown patterns and a statistical prior. Biomed. Opt. Express 8, 695–711 (2017). 74. Shroff, H., White, H. & Betzig, E. Photoactivated Localization Microscopy (PALM) of Adhesion Complexes. Curr. Protoc. Cell Biol. 58, 4.21.1-4.21.28 (2013). 75. Xu, J., Ma, H. & Liu, Y. Stochastic optical reconstruction microscopy (STORM). Curr. Protoc. Cytom. 81, 12.46.1-12.46.27 (2017). 76. Kuang, C. et al. Breaking the Diffraction Barrier Using Fluorescence Emission Difference Microscopy. Sci. Rep. 3, 1441 (2013). 77. Kang, M.-S. et al. Accuracy improvement of quantification information using super-resolution with convolutional neural network for microscopy images. Biomed. Signal Process. Control 58, 101846 (2020). 78. Wang, Z., Chen, J. & Hoi, S. C. H. Deep Learning for Image Super-resolution: A Survey. Preprint at http://arxiv.org/abs/1902.06068 (2020). 79. Digital Imaging Tutorial - Basic Terminology. Cornell University Library/ Research Department http://preservationtutorial.library.cornell.edu/tutorial/ (2000). 80. Rodríguez-Sevilla, P., Thompson, S. A. & Jaque, D. Multichannel Fluorescence Microscopy: Advantages of Going beyond a Single Emission. Adv. NanoBiomed Res. 2, 2100084 (2022). 81. Qi, G. et al. Nucleus and Mitochondria Targeting Theranostic Plasmonic SERS Nanoprobes Reveal Molecular Stress Response Difference in Hyperthermia Cell Death between Cancerous and Normal Cells. Anal. Chem. 90, (2018). 82. Basu, S. S. et al. Rapid MALDI mass spectrometry imaging for surgical pathology. Npj Precis. Oncol. 3, 1–5 (2019). 83. Lagache, T., Sauvonnet, N., Danglot, L. & Olivo-Marin, J.-C. Statistical analysis of molecule colocalization in bioimaging. Cytometry A 87, 568–579 (2015). 84. Aron, M. et al. Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order. BMC Bioinformatics 18, 254 (2017). 85. Wang, F., Oh, T. W., Vergara-Niedermayr, C., Kurc, T. & Saltz, J. Managing and Querying Whole Slide Images. Proc. SPIE 8319, 83190J (2012). 86. Farahani, N., Parwani, A. V. & Pantanowitz, L. Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol. Lab. Med. Int. 7, 23–33 (2015). 87. Antonelli, L., Guarracino, M. R., Maddalena, L. & Sangiovanni, M. Integrating imaging and omics data: A review. Biomed. Signal Process. Control 52, 264–280 (2019). 88. Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of Image Analysis. Nat. Methods 9, 671–675 (2012). 89. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012). 90. Seibert, J. A., Boone, J. M. & Lindfors, K. K. Flat-field correction technique for digital detectors. in (eds. Dobbins Iii, J. T. & Boone, J. M.) 348 (San Diego, CA, 1998). doi:10.1117/12.317034. 91. Ibraheem, N. A., Hasan, M. M., Khan, R. Z. & Mishra, P. K. Understanding Color Models: A Review. 2, (2012). 92. Standard RGB Color Spaces. Proc ISTSID 7th Color Imaging Conf. (1999). 93. HTML Color Codes. HTML Color Codes https://htmlcolorcodes.com/. 94. Harrower, M. & Brewer, C. A. ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps. Cartogr. J. 40, 27–37 (2003). 95. Püspöki, Z., Storath, M., Sage, D. & Unser, M. Transforms and Operators for Directional Bioimage Analysis: A Survey. in Focus on Bio-Image Informatics (eds. De Vos, W. H., Munck, S. & Timmermans, J.-P.) vol. 219 69–93 (Springer International Publishing, Cham, 2016). 96. Foot, N. C. The Masson Trichrome Staining Methods in Routine Laboratory Use. Stain Technol. 8, 101–110 (1933). 97. Glasbey, C., van der Heijden, G., Toh, V. F. K. & Gray, A. Colour displays for categorical images. Color Res. Appl. 32, 304–309 (2007). 98. Landini, G., Martinelli, G. & Piccinini, F. Colour deconvolution: stain unmixing in histological imaging. Bioinformatics 37, 1485–1487 (2021). 99. Ruifrok, A. & Johnston, D. Ruifrok AC, Johnston DA. Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol 23: 291-299. Anal. Quant. Cytol. Histol. Int. Acad. Cytol. Am. Soc. Cytol. 23, 291–9 (2001). 100. Bankhead, P. et al. QuPath: Open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017). 101. Linkert, M. et al. Metadata matters: access to image data in the real world. J. Cell Biol. 189, 777–782 (2010). 102. Bio-formats. https://www.openmicroscopy.org/bio-formats/. 103. Vorkel, D. & Haase, R. GPU-Accelerating ImageJ Macro Image Processing Workflows Using CLIJ. in Bioimage Data Analysis Workflows ‒ Advanced Components and Methods (eds. Miura, K. & Sladoje, N.) 89–114 (Springer International Publishing, Cham, 2022). doi:10.1007/978-3-030-76394-7_5. 104. Ettinger, A. & Wittmann, T. Fluorescence Live Cell Imaging. Methods Cell Biol. 123, 77–94 (2014). 105. Model, M. Intensity Calibration and Flat‐Field Correction for Fluorescence Microscopes. Curr. Protoc. Cytom. 68, (2014). 106. Wang, Q., Niemi, J., Tan, C.-M., You, L. & West, M. Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy. Cytometry A 77A, 101–110 (2010). 107. Coste, A. CS6640: Image Processing Project 2 Filtering, Edge detection and Template matching. in (2012). 108. Cromey, D. W. Avoiding Twisted Pixels: Ethical Guidelines for the Appropriate Use and Manipulation of Scientific Digital Images. Sci. Eng. Ethics 16, 639–667 (2010). 109. Kafadar, Ö. Applications of the Kuwahara and Gaussian filters on potential field data. J. Appl. Geophys. 198, 104583 (2022). 110. Engel, W. GPU Pro: Advanced Rendering Techniques. (CRC Press, 2010). 111. Kyprianidis, J. E., Semmo, A., Kang, H. & Döllner, J. Anisotropic Kuwahara Filtering with Polynomial Weighting Functions. (2010). 112. Lucas, A. M. et al. Open-source deep-learning software for bioimage segmentation. Mol. Biol. Cell 32, 823–829 (2021). 113. Russ, J. C. The Image Processing Handbook. (CRC Press, Boca Raton, 2006). doi:10.1201/9780203881095. 114. John McCarthy, Marvin L. Minsky, Nathaniel Rochester, & Claude E. Shannon. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. (1955). 115. Legg, S. & Hutter, M. A Collection of Definitions of Intelligence. Preprint at http://arxiv.org/abs/0706.3639 (2007). 116. Sheikh, H., Prins, C. & Schrijvers, E. Artificial Intelligence: Definition and Background. in Mission AI: The New System Technology (eds. Sheikh, H., Prins, C. & Schrijvers, E.) 15–41 (Springer International Publishing, Cham, 2023). doi:10.1007/978-3-031-21448-6_2. 117. Fjelland, R. Why general artificial intelligence will not be realized. Humanit. Soc. Sci. Commun. 7, 1–9 (2020). 118. Bini, S. A. Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care? J. Arthroplasty 33, 2358–2361 (2018). 119. Jordan, M. I. & Mitchell, T. M. Machine learning: Trends, perspectives, and prospects. Science 349, 255–260 (2015). 120. Greener, J. G., Kandathil, S. M., Moffat, L. & Jones, D. T. A guide to machine learning for biologists. Nat. Rev. Mol. Cell Biol. 23, 40–55 (2022). 121. Badillo, S. et al. An Introduction to Machine Learning. Clin. Pharmacol. Ther. 107, 871–885 (2020). 122. Montesinos López, O. A., Montesinos López, A. & Crossa, J. Fundamentals of Artificial Neural Networks and Deep Learning. in Multivariate Statistical Machine Learning Methods for Genomic Prediction (eds. Montesinos López, O. A., Montesinos López, A. & Crossa, J.) 379–425 (Springer International Publishing, Cham, 2022). doi:10.1007/978-3-030-89010-0_10. 123. Deep Learning in a Nutshell: Core Concepts. NVIDIA Technical Blog https://developer.nvidia.com/blog/deep-learning-nutshell-core-concepts/ (2015). 124. Suzuki, K. Overview of deep learning in medical imaging. Radiol. Phys. Technol. 10, 257–273 (2017). 125. Milletari, F., Navab, N. & Ahmadi, S.-A. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Preprint at https://doi.org/10.48550/arXiv.1606.04797 (2016). 126. Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (eds. Navab, N., Hornegger, J., Wells, W. M. & Frangi, A. F.) 234–241 (Springer International Publishing, Cham, 2015). doi:10.1007/978-3-319-24574-4_28. 127. Fukushima, K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 193–202 (1980). 128. Gyawali, D. Comparative Analysis of CPU and GPU Profiling for Deep Learning Models. Preprint at http://arxiv.org/abs/2309.02521 (2023). 129. Machine Learning Service - Amazon SageMaker - AWS. https://aws.amazon.com/sagemaker/. 130. Google Colaboratory. https://colab.research.google.com/. 131. Kaggle: Your Machine Learning and Data Science Community. https://www.kaggle.com/. 132. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015). 133. Uhlmann, V., Donati, L. & Sage, D. A Practical Guide to Supervised Deep Learning for Bioimage Analysis: Challenges and good practices. IEEE Signal Process. Mag. 39, 73–86 (2022). 134. Gunning, D., Vorm, E., Wang, J. Y. & Turek, M. DARPA's explainable AI (XAI) program: A retrospective. Appl. AI Lett. 2, e61 (2021). 135. van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A. & Viergever, M. A. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med. Image Anal. 79, 102470 (2022). 136. Exploring the Web of Science Core Collection | Sciendo. https://sciendo.com/blog/exploring-the-web-of-science-core-collection (2023). 137. Verwer, Groen, Ekkers, & Ellens. Image processing on personal computers. in IEEE International Conference on Systems Engineering 183–186 (IEEE, Fairborn, OH, USA, 1989). doi:10.1109/ICSYSE.1989.48650. 138. Wilhelm, S. et al. Analysis of nanoparticle delivery to tumours. Nat. Rev. Mater. 1, 1–12 (2016). 139. Prado, C. M. et al. Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol. 9, 629–635 (2008). 140. Bhatia, S. N. & Ingber, D. E. Microfluidic organs-on-chips. Nat. Biotechnol. 32, 760–772 (2014). 141. Pampaloni, F., Reynaud, E. G. & Stelzer, E. H. K. The third dimension bridges the gap between cell culture and live tissue. Nat. Rev. Mol. Cell Biol. 8, 839–845 (2007). 142. O'Rourke, D. M. et al. A single dose of peripherally infused EGFRvIII-directed CAR T cells mediates antigen loss and induces adaptive resistance in patients with recurrent glioblastoma. Sci. Transl. Med. 9, eaaa0984 (2017). 143. Kim, P. MATLAB Deep Learning. (Apress, Berkeley, CA, 2017). doi:10.1007/978-1-4842-2845-6. 144. Schroeder, A. B. et al. The ImageJ ecosystem: Open-source software for image visualization, processing, and analysis. Protein Sci. 30, 234–249 (2021). 145. Core Team, R. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna Austria URL Httpswww R-Proj. OrgGoogle Sch. (2021). 146. RStudio Team. RStudio: Integrated Development Environment for R. (RStudio, PBC., Boston, MA, 2021). 147. Aria, M. & Cuccurullo, C. bibliometrix: An R-tool for comprehensive science mapping analysis. J. Informetr. 11, 959–975 (2017). 148. Asanovic, K. et al. The Parallel Computing Laboratory at U.C. Berkeley: A Research Agenda Based on the Berkeley View. (2008). 149. Schindler, D., Bensmann, F., Dietze, S. & Krüger, F. The role of software in science: a knowledge graph-based analysis of software mentions in PubMed Central. PeerJ Comput. Sci. 8, e835 (2022). 150. Long, R. E. et al. Scientific and Regulatory Policy Committee (SRPC) Paper: Validation of Digital Pathology Systems in the Regulated Nonclinical Environment. Toxicol. Pathol. 41, 115–124 (2013). 151. Paul-Gilloteaux, P. Bioimage informatics: Investing in software usability is essential. PLOS Biol. 21, e3002213 (2023). 152. Chlipala, E. A. et al. Impact of Preanalytical Factors During Histology Processing on Section Suitability for Digital Image Analysis. Toxicol. Pathol. 49, 755–772 (2021). 153. Hayes, T. R. et al. Biological Distribution and Metabolic Profiles of Carbon-11 and Fluorine-18 Tracers of VX- and Sarin-Analogs in Sprague–Dawley Rats. Chem. Res. Toxicol. 34, 63–69 (2021). 154. Basu, A., Dydowiczová, A., Trosko, J. E., Bláha, L. & Babica, P. Ready to go 3D? A semi-automated protocol for microwell spheroid arrays to increase scalability and throughput of 3D cell culture testing. Toxicol. Mech. Methods 30, 590–604 (2020). 155. Adomshick, V., Pu, Y. & Veiga-Lopez, A. Automated Lipid Droplet Quantification System for Phenotypic Analysis of Adipocytes using CellProfiler. Toxicol. Mech. Methods 30, 378–387 (2020). 156. Nault, R., Colbry, D., Brandenberger, C., Harkema, J. R. & Zacharewski, T. R. Development of a Computational High-Throughput Tool for the Quantitative Examination of Dose-Dependent Histological Features. Toxicol. Pathol. 43, 366–375 (2015). 157. McCollum, C. W. et al. Identification of vascular disruptor compounds by analysis in zebrafish embryos and mouse embryonic endothelial cells. Reprod. Toxicol. 70, 60–69 (2017). 158. Nyska, A. et al. Electron Microscopy of Wet Tissues: A Case Study in Renal Pathology. Toxicol. Pathol. 32, 357–363 (2004). 159. He, K., Zhang, X., Ren, S. & Sun, J. Deep Residual Learning for Image Recognition. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778 (IEEE, Las Vegas, NV, USA, 2016). doi:10.1109/CVPR.2016.90. 160. Jia, Y. et al. Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv.org https://arxiv.org/abs/1408.5093v1 (2014). 161. Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. arXiv.org https://arxiv.org/abs/1412.6980v9 (2014). 162. Nilsson, A. et al. Investigating Nephrotoxicity of Polymyxin Derivatives by Mapping Renal Distribution Using Mass Spectrometry Imaging. Chem. Res. Toxicol. 28, 1823–1830 (2015). 163. Andersson, M., Karlsson, O. & Brandt, I. The environmental neurotoxin β-N-methylamino-l-alanine (l-BMAA) is deposited into birds' eggs. Ecotoxicol. Environ. Saf. 147, 720–724 (2018). 164. Danielsson, B. R., Sköld, A.-C., Johansson, A., Dillner, B. & Blomgren, B. Teratogenicity by the hERG potassium channel blocking drug almokalant: use of hypoxia marker gives evidence for a hypoxia-related mechanism mediated via embryonic arrhythmia. Toxicol. Appl. Pharmacol. 193, 168–176 (2003). 165. Ostling, O. & Johanson, K. J. Microelectrophoretic study of radiation-induced DNA damages in individual mammalian cells. Biochem. Biophys. Res. Commun. 123, 291–298 (1984). 166. Hinojosa, M. G. et al. Evaluation of mRNA markers in differentiating human SH-SY5Y cells for estimation of developmental neurotoxicity. NeuroToxicology 97, 65–77 (2023). 167. Hu, C., Hsiao, Z. H., Yin, L. & Yu, X. The role of small GTPases in bisphenol AF-induced multinucleation in comparison with dibutyl phthalate in the male germ cells. Toxicol. Sci. 192, 43–58 (2023). 168. El Hayek, E. et al. Photoaging of polystyrene microspheres causes oxidative alterations to surface physicochemistry and enhances airway epithelial toxicity. Toxicol. Sci. 193, 90–102 (2023). 169. Ibabe, A., Herrero, A. & Cajaraville, M. P. Modulation of peroxisome proliferator-activated receptors (PPARs) by PPARα- and PPARγ-specific ligands and by 17β-estradiol in isolated zebrafish hepatocytes. Toxicol. Ин витро 19, 725–735 (2005). 170. Marigómez, I., Izagirre, U. & Lekube, X. Lysosomal enlargement in digestive cells of mussels exposed to cadmium, benzo[a]pyrene and their combination. Comp. Biochem. Physiol. Toxicol. Pharmacol. CBP 141, 188–193 (2005). 171. Alvarez-Mora, I. et al. SETApp: A machine learning and image analysis based application to automate the sea urchin embryo test. Ecotoxicol. Environ. Saf. 241, 113728 (2022). 172. Frieauff, W., Pötter-Locher, F., Cordier, A. & Suter, W. Automatic analysis of the ин витро micronucleus test on V79 cells. Mutat. Res. Toxicol. Environ. Mutagen. 413, 57–68 (1998). 173. Frieauff, W., Hartmann, A. & Suter, W. Automatic analysis of slides processed in the Comet assay. Mutagenesis 16, 133–137 (2001). 174. Das, K. P., Freudenrich, T. M. & Mundy, W. R. Assessment of PC12 cell differentiation and neurite growth: a comparison of morphological and neurochemical measures. Neurotoxicol. Teratol. 26, 397–406 (2004). 175. Teixidó, E. et al. Automated Morphological Feature Assessment for Zebrafish Embryo Developmental Toxicity Screens. Toxicol. Sci. 167, 438–449 (2019). 176. Scholz, S. et al. Analysis of estrogenic effects by quantification of green fluorescent protein in juvenile fish of a transgenic medaka. Environ. Toxicol. Chem. 24, 2553–2561 (2005). 177. Lee, J., Escher, B. I., Scholz, S. & Schlichting, R. Inhibition of neurite outgrowth and enhanced effects compared to baseline toxicity in SH-SY5Y cells. Arch. Toxicol. 96, 1039–1053 (2022). 178. Fischer, F. C. et al. Cellular Uptake Kinetics of Neutral and Charged Chemicals in ин витро Assays Measured by Fluorescence Microscopy. Chem. Res. Toxicol. 31, 646–657 (2018). 179. Teixidó, E., Kieβling, T. R., Klüver, N. & Scholz, S. Grouping of chemicals into mode of action classes by automated effect pattern analysis using the zebrafish embryo toxicity test. Arch. Toxicol. 96, 1353–1369 (2022). 180. Forchhammer, L. et al. Variation in the measurement of DNA damage by comet assay measured by the ECVAG† inter-laboratory validation trial. Mutagenesis 25, 113–123 (2010). 181. Azqueta, A. et al. The influence of scoring method on variability in results obtained with the comet assay. Mutagenesis 26, 393–399 (2011). 182. Møller, P. et al. Visual comet scoring revisited: a guide to scoring comet assay slides and obtaining reliable results. Mutagenesis 38, 253–263 (2023). 183. Potier, M., Lakhdar, B., L'azou, B. & Cambar, J. Inhibition of cyclosporin-induced ин витро glomerular contraction by theophylline. Toxicol. Ин витро 9, 473–476 (1995). 184. Singh, N. P., McCoy, M. T., Tice, R. R. & Schneider, E. L. A simple technique for quantitation of low levels of DNA damage in individual cells. Exp. Cell Res. 175, 184–191 (1988). 185. Tice, R. R. et al. Single cell gel/comet assay: Guidelines for ин витро and ин виво genetic toxicology testing. Environ. Mol. Mutagen. 35, 206–221 (2000). 186. Fairbairn, D. W., Olive, P. L. & O'Neill, K. L. The comet assay: a comprehensive review. Mutat. Res. Genet. Toxicol. 339, 37–59 (1995). 187. Olive, P. L., Banáth, J. P. & Durand, R. E. Heterogeneity in Radiation-Induced DNA Damage and Repair in Tumor and Normal Cells Measured Using the 'Comet' Assay. Radiat. Res. 122, 86–94 (1990). 188. Collins, A. R., Ai-guo, M. & Duthie, S. J. The kinetics of repair of oxidative DNA damage (strand breaks and oxidised pyrimidines) in human cells. Mutat. Res. Repair 336, 69–77 (1995). 189. Jaggi, B., Poon, S. S. S., Macaulay, C. & Palcic, B. Imaging system for morphometric assessment of absorption or fluorescence in stained cells. Cytometry 9, 566–572 (1988). 190. McKelvey-Martin, V. J. et al. The single cell gel electrophoresis assay (comet assay): a European review. Mutat. Res. 288, 47–63 (1993). 191. Maron, D. M. & Ames, B. N. Revised methods for the Salmonella mutagenicity test. Mutat. Res. Mutagen. Relat. Subj. 113, 173–215 (1983). 192. Collins, A. R., Cadet, J., Mőller, L., Poulsen, H. E. & Viña, J. Are we sure we know how to measure 8-oxo-7,8-dihydroguanine in DNA from human cells? Arch. Biochem. Biophys. 423, 57–65 (2004). 193. Lowe, D. M., Moore, M. N. & Clarke, K. R. Effects of oil on digestive cells in mussels: Quantitative alterations in cellular and lysosomal structure. Aquat. Toxicol. 1, 213–226 (1981). 194. Moore, M. Cytochemical responses of the lysosomal system and NADPH-ferrihemoprotein reductase in molluscan digestive cells to environmental and experimental exposure to xenobiotics. Mar. Ecol. Prog. Ser. 46, 81–89 (1988). 195. Etxeberria, M., Sastre, I., Cajaraville, M. P. & Marig�mez, I. Digestive lysosome enlargement induced by experimental exposure to metals (Cu, Cd, and Zn) in mussels collected from a zinc-polluted site. Arch. Environ. Contam. Toxicol. 27, (1994). 196. Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254 (1976). 197. Laemmli, U. K. Cleavage of Structural Proteins during the Assembly of the Head of Bacteriophage T4. Nature 227, 680–685 (1970). 198. Romagna, F. & Staniforth, C. D. The automated bone marrow micronucleus test. Mutat. Res. Mol. Mech. Mutagen. 213, 91–104 (1989). 199. Frieauff, W. & Romagna, F. Technical aspects of automatic micronucleus analysis in rodent bone marrow assays. Cell Biol. Toxicol. 10, 283–289 (1994). 200. Fenech, M. The ин витро micronucleus technique. Mutat. Res. Mol. Mech. Mutagen. 455, 81–95 (2000). 201. Fenech, M. et al. HUMN project: detailed description of the scoring criteria for the cytokinesis-block micronucleus assay using isolated human lymphocyte cultures. Mutat. Res. 534, 65–75 (2003). 202. Diaz, D., Scott, A., Carmichael, P., Shi, W. & Costales, C. Evaluation of an automated ин витро micronucleus assay in CHO-K1 cells. Mutat. Res. Toxicol. Environ. Mutagen. 630, 1–13 (2007). 203. Decordier, I. et al. Automated image analysis of cytokinesis-blocked micronuclei: an adapted protocol and a validated scoring procedure for biomonitoring. Mutagenesis 24, 85–93 (2009). 204. Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods San Diego Calif 25, 402–408 (2001). 205. Aeffner, F. et al. Commentary: Roles for Pathologists in a High-throughput Image Analysis Team. Toxicol. Pathol. 44, 825–834 (2016). 206. Aeffner, F. et al. The Gold Standard Paradox in Digital Image Analysis: Manual Versus Automated Scoring as Ground Truth. Arch. Pathol. Lab. Med. 141, 1267–1275 (2017). 207. Grandjean, P., Harari, R., Barr, D. B. & Debes, F. Pesticide exposure and stunting as independent predictors of neurobehavioral deficits in Ecuadorian school children. Pediatrics 117, e546-556 (2006). 208. Moors, M. et al. Human Neurospheres as Three-Dimensional Cellular Systems for Developmental Neurotoxicity Testing. Environ. Health Perspect. 117, 1131–1138 (2009). 209. Siddique, N., Paheding, S., Elkin, C. P. & Devabhaktuni, V. U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications. IEEE Access 9, 82031–82057 (2021). 210. Gauthier, B. E. et al. Toxicologic Pathology Forum*: Opinion on Integrating Innovative Digital Pathology Tools in the Regulatory Framework. Toxicol. Pathol. 47, 436–443 (2019). 211. Kuklyte, J. et al. Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies. Toxicol. Pathol. 49, 815–842 (2021). 212. Pischon, H. et al. Artificial Intelligence in Toxicologic Pathology: Quantitative Evaluation of Compound-Induced Hepatocellular Hypertrophy in Rats. Toxicol. Pathol. 49, 928–937 (2021). 213. Schumacher, V. L. et al. The Application, Challenges, and Advancement Toward Regulatory Acceptance of Digital Toxicologic Pathology: Results of the 7th ESTP International Expert Workshop (September 20-21, 2019). Toxicol. Pathol. 49, 720–737 (2021). 214. Saravanan, C. et al. Meeting Report: Tissue-based Image Analysis. Toxicol. Pathol. 45, 983–1003 (2017). 215. Lindauer, K., Bartels, T., Scherer, P. & Kabiri, M. Development and Validation of an Image Analysis System for the Measurement of Cell Proliferation in Mammary Glands of Rats. Toxicol. Pathol. 47, 634–644 (2019). 216. Yin, L. et al. High-Content Image-Based Single-Cell Phenotypic Analysis for the Testicular Toxicity Prediction Induced by Bisphenol A and Its Analogs Bisphenol S, Bisphenol AF, and Tetrabromobisphenol A in a Three-Dimensional Testicular Cell Co-culture Model. Toxicol. Sci. 173, 313–335 (2020). 217. O'Brien, P. J. et al. High concordance of drug-induced human hepatotoxicity with ин витро cytotoxicity measured in a novel cell-based model using high content screening. Arch. Toxicol. 80, 580–604 (2006). 218. Harrill, J. A., Freudenrich, T. M., Machacek, D. W., Stice, S. L. & Mundy, W. R. Quantitative assessment of neurite outgrowth in human embryonic stem cell-derived hN2TM cells using automated high-content image analysis. NeuroToxicology 31, 277–290 (2010). 219. Verma, J. R. et al. Evaluation of the automated MicroFlow® and MetaferTM platforms for high-throughput micronucleus scoring and dose response analysis in human lymphoblastoid TK6 cells. Arch. Toxicol. 91, 2689–2698 (2017). 220. Pointon, A. et al. From the Cover: High-Throughput Imaging of Cardiac Microtissues for the Assessment of Cardiac Contraction during Drug Discovery. Toxicol. Sci. 155, 444–457 (2017). 221. Wink, S., Hiemstra, S., Herpers, B. & van de Water, B. High-content imaging-based BAC-GFP toxicity pathway reporters to assess chemical adversity liabilities. Arch. Toxicol. 91, 1367–1383 (2017). 222. Kozlowski, C., Brumm, J. & Cain, G. An Automated Image Analysis Method to Quantify Veterinary Bone Marrow Cellularity on H&E Sections. Toxicol. Pathol. 46, 324–335 (2018). 223. Ishii, Y. et al. Visualization of the distribution of anthraquinone components from madder roots in rat kidneys by desorption electrospray ionization-time-of-flight mass spectrometry imaging. Food Chem. Toxicol. Int. J. Publ. Br. Ind. Biol. Res. Assoc. 161, 112851 (2022). 224. Asaoka, Y. et al. Histopathological image analysis of chemical-induced hepatocellular hypertrophy in mice. Exp. Toxicol. Pathol. 68, 233–239 (2016). 225. Ramot, Y. et al. Microscope-Based Automated Quantification of Liver Fibrosis in Mice Using a Deep Learning Algorithm. Toxicol. Pathol. 49, 1126–1133 (2021). 226. Tokarz, D. A. et al. Using Artificial Intelligence to Detect, Classify, and Objectively Score Severity of Rodent Cardiomyopathy. Toxicol. Pathol. 49, 888–896 (2021). 227. Zuraw, A. et al. Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology. Toxicol. Pathol. 49, 773–783 (2021). 228. Srivastava, A. et al. Quantitative Neurotoxicology: An Assessment of the Neurotoxic Profile of Kainic Acid in Sprague Dawley Rats. Int. J. Toxicol. 39, 294–306 (2020). 229. Yoshikawa, T. et al. Current status of pathological image analysis technology in pharmaceutical companies: a questionnaire survey of the Japan Pharmaceutical Manufacturers Association. J. Toxicol. Pathol. 33, 131–139 (2020). 230. Nolte, T., Kaufmann, W., Schorsch, F., Soames, T. & Weber, E. Standardized assessment of cell proliferation: The approach of the RITA-CEPA working group. Exp. Toxicol. Pathol. 57, 91–103 (2005). 231. Horai, Y. et al. Quantification of histopathological findings using a novel image analysis platform. J. Toxicol. Pathol. 32, 319–327 (2019). 232. Petroff, R. et al. Chronic, low-level oral exposure to marine toxin, domoic acid, alters whole brain morphometry in nonhuman primates. NeuroToxicology 72, 114–124 (2019). 233. Ramm, S. et al. A Systems Toxicology Approach for the Prediction of Kidney Toxicity and Its Mechanisms Ин витро. Toxicol. Sci. 169, 54–69 (2019). 234. Kozlowski, C. et al. Proof of Concept for an Automated Image Analysis Method to Quantify Rat Bone Marrow Hematopoietic Lineages on H&E Sections. Toxicol. Pathol. 46, 336–347 (2018). 235. Stock, V., Sutter, A., Raschke, M. & Queisser, N. A tripartite mode of action approach for investigating the impact of aneugens on tubulin polymerization. Environ. Mol. Mutagen. 59, 188–201 (2018). 236. Wink, S., Hiemstra, S. W., Huppelschoten, S., Klip, J. E. & van de Water, B. Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury. Arch. Toxicol. 92, 1797–1814 (2018). 237. Mundy, W. R., Radio, N. M. & Freudenrich, T. M. Neuronal models for evaluation of proliferation ин витро using high content screening. Toxicology 270, 121–130 (2010). 238. Lazzari, M., Bettini, S., Milani, L., Maurizii, M. G. & Franceschini, V. Differential response of olfactory sensory neuron populations to copper ion exposure in zebrafish. Aquat. Toxicol. 183, 54–62 (2017). 239. Zhu, Y.-J. et al. Molecular and toxicologic research in newborn hypospadiac male rats following in utero exposure to di-n-butyl phthalate (DBP). Toxicology 260, 120–125 (2009). 240. Bigley, A. L., Klein, S. K., Davies, B., Williams, L. & Rudmann, D. G. Using Automated Image Analysis Algorithms to Distinguish Normal, Aberrant, and Degenerate Mitotic Figures Induced by Eg5 Inhibition. Toxicol. Pathol. 44, 663–672 (2016). 241. Deal, S. et al. Development of a quantitative morphological assessment of toxicant-treated zebrafish larvae using brightfield imaging and high-content analysis. J. Appl. Toxicol. JAT 36, 1214–1222 (2016). 242. Garman, R. H., Li, A. A., Kaufmann, W., Auer, R. N. & Bolon, B. Recommended Methods for Brain Processing and Quantitative Analysis in Rodent Developmental Neurotoxicity Studies. Toxicol. Pathol. 44, 14–42 (2016). 243. Bernardi, M. et al. Absence of micronucleus formation in CHO-K1 cells cultivated in platelet lysate enriched medium. Exp. Toxicol. Pathol. 66, 111–116 (2014). 244. Hammad, S. et al. Protocols for staining of bile canalicular and sinusoidal networks of human, mouse and pig livers, three-dimensional reconstruction and quantification of tissue microarchitecture by image processing and analysis. Arch. Toxicol. 88, 1161–1183 (2014). 245. Machado, M. D. & Soares, E. V. Modification of cell volume and proliferative capacity of Pseudokirchneriella subcapitata cells exposed to metal stress. Aquat. Toxicol. 147, 1–6 (2014). 246. Nakanishi, T., Nirasawa, T. & Takubo, T. Quantitative Mass Barcode-Like Image of Nicotine in Single Longitudinally Sliced Hair Sections from Long-Term Smokers by Matrix-Assisted Laser Desorption Time-of-Flight Mass Spectrometry Imaging. J. Anal. Toxicol. 38, 349–353 (2014). 247. Axelrad, J. C., Howard, C. V. & McLean, W. G. Interactions between pesticides and components of pesticide formulations in an ин витро neurotoxicity test. Toxicology 173, 259–268 (2002). 248. Kiskinis, E., Suter, W. & Hartmann, A. High throughput Comet assay using 96-well plates. Mutagenesis 17, 37–43 (2002). 249. Riecke, K. et al. Low doses of 2,3,7,8-tetrachlorodibenzo-p-dioxin increase transforming growth factor β and cause myocardial fibrosis in marmosets (Callithrix jacchus). Arch. Toxicol. 76, 360–366 (2002). 250. Aung, K. H. et al. Inhibition of neurite outgrowth and alteration of cytoskeletal gene expression by sodium arsenite. NeuroToxicology 34, 226–235 (2013). 251. Frieauff, W., Martus, H. J., Suter, W. & Elhajouji, A. Automatic analysis of the micronucleus test in primary human lymphocytes using image analysis. Mutagenesis 28, 15–23 (2013). 252. Hall, A. P., Davies, W., Stamp, K., Clamp, I. & Bigley, A. Comparison of computerized image analysis with traditional semiquantitative scoring of Perls' Prussian Blue stained hepatic iron deposition. Toxicol. Pathol. 41, 992–1000 (2013). 253. Mercer, R. R. et al. Distribution and fibrotic response following inhalation exposure to multi-walled carbon nanotubes. Part. Fibre Toxicol. 10, 33 (2013). 254. Wakui, S. et al. Nuclear Morphometric Analysis of Leydig Cells of Male Pubertal Rats Exposed In Utero to Di(n-butyl) Phthalate. J. Toxicol. Pathol. 26, 439–446 (2013). 255. Wakui, S. et al. Retraction: Nuclear Morphometric Analysis of Leydig Cells of Male Pubertal Rats Exposed In Utero to Di(n-butyl) Phthalate. J. Toxicol. Pathol. 36, R1 (2023). 256. Johnson, I., Harman, M., Forrow, D. & Norris, M. An assessment of the feasibility of using image analysis in the oyster embryo-larval development test. Environ. Toxicol. 16, 68–77 (2001). 257. Beasley, A., Elrod-Erickson, M. & Otter, R. R. Consistency of morphological endpoints used to assess developmental timing in zebrafish (Danio rerio) across a temperature gradient. Reprod. Toxicol. 34, 561–567 (2012). 258. Foit, K., Kaske, O., Wahrendorf, D.-S., Duquesne, S. & Liess, M. Automated Nanocosm test system to assess the effects of stressors on two interacting populations. Aquat. Toxicol. 109, 243–249 (2012). 259. Singh, R. P. & Ramarao, P. Cellular uptake, intracellular trafficking and cytotoxicity of silver nanoparticles. Toxicol. Lett. 213, 249–259 (2012). 260. Swedin, L. et al. Pulmonary exposure to single-walled carbon nanotubes does not affect the early immune response against Toxoplasma gondii. Part. Fibre Toxicol. 9, 16 (2012). 261. L'Azou, B. et al. Ин витро models to study mechanisms involved in cyclosporine A-mediated glomerular contraction. Arch. Toxicol. 73, 337–345 (1999). 262. Bolon, B. et al. Continuing Education Course #3: Current Practices and Future Trends in Neuropathology Assessment for Developmental Neurotoxicity Testing. Toxicol. Pathol. 39, 289–293 (2011). 263. Dunstan, R. W., Wharton, K. A., Quigley, C. & Lowe, A. The use of immunohistochemistry for biomarker assessment--can it compete with other technologies? Toxicol. Pathol. 39, 988–1002 (2011). 264. Jiang, J.-T. et al. Prenatal exposure to di-n-butyl phthalate induces anorectal malformations in male rat offspring. Toxicology 290, 322–326 (2011). 265. Miki, A. et al. Imaging of methamphetamine incorporated into hair by MALDI-TOF mass spectrometry. Forensic Toxicol. 29, 111–116 (2011). 266. Zhang, Q., Hitchins, V. M., Schrand, A. M., Hussain, S. M. & Goering, P. L. Uptake of gold nanoparticles in murine macrophage cells without cytotoxicity or production of pro-inflammatory mediators. Nanotoxicology 5, 284–295 (2011). 267. McGee, M. R. et al. Predator avoidance performance of larval fathead minnows (Pimephales promelas) following short-term exposure to estrogen mixtures. Aquat. Toxicol. Amst. Neth. 91, 355–361 (2009). 268. Calas, A.-G. et al. Chronic exposure to glufosinate-ammonium induces spatial memory impairments, hippocampal MRI modifications and glutamine synthetase activation in mice. NeuroToxicology 29, 740–747 (2008). 269. Mundy, W. R., Robinette, B., Radio, N. M. & Freudenrich, T. M. Protein biomarkers associated with growth and synaptogenesis in a cell culture model of neuronal development. Toxicology 249, 220–229 (2008). 270. Pejchal, J., Osterreicher, J., Kassa, J., Tichý, A. & Mokrý, J. Activation of mitogen activated protein kinase (MAPK) pathways after soman poisoning in rat cerebellar granule neurons. J. Appl. Toxicol. JAT 28, 689–693 (2008). 271. Guerlet, E., Ledy, K. & Giambérini, L. Field application of a set of cellular biomarkers in the digestive gland of the freshwater snail Radix peregra (Gastropoda, Pulmonata). Aquat. Toxicol. 77, 19–32 (2006). 272. Alvarado, N. E., Buxens, A., Mazón, L. I., Marigómez, I. & Soto, M. Cellular biomarkers of exposure and biological effect in hepatocytes of turbot (Scophthalmus maximus) exposed to Cd, Cu and Zn and after depuration. Aquat. Toxicol. 74, 110–125 (2005). 273. Dhar, P., Jaitley, M., Kalaivani, M. & Mehra, R. D. Preliminary Morphological and Histochemical Changes in Rat Spinal Cord Neurons Following Arsenic Ingestion. NeuroToxicology 26, 309–320 (2005). 274. Mitić, T. & McKay, J. S. Immunohistochemical analysis of acetylation, proliferation, mitosis, and apoptosis in tumor xenografts following administration of a histone deacetylase inhibitor--a pilot study. Toxicol. Pathol. 33, 792–799 (2005). 275. Oberemm, A. et al. Differential signatures of protein expression in marmoset liver and thymus induced by single-dose TCDD treatment. Toxicology 206, 33–48 (2005). 276. Colton, H. M. et al. Visualization and Quantitation of Peroxisomes Using Fluorescent Nanocrystals: Treatment of Rats and Monkeys with Fibrates and Detection in the Liver. Toxicol. Sci. 80, 183–192 (2004). 277. Garcia, G. et al. Study of Tyrosine Hydroxylase Immunoreactive Neurons in Neonate Rats Lactationally Exposed to 2,4-Dichlorophenoxyacetic Acid. NeuroToxicology 25, 951–957 (2004). 278. Duez, P., Dehon, G., Kumps, A. & Dubois, J. Statistics of the Comet assay: a key to discriminate between genotoxic effects. Mutagenesis 18, 159–166 (2003). 279. Cheng, S. H., Chan, P. K. & Wu, R. S. S. The use of microangiography in detecting aberrant vasculature in zebrafish embryos exposed to cadmium. Aquat. Toxicol. 52, 61–71 (2001). 280. Tahedl, H. & Häder, D.-P. Automated Biomonitoring Using Real Time Movement Analysis of Euglena gracilis. Ecotoxicol. Environ. Saf. 48, 161–169 (2001). 281. Winzer, K., Becker, W., Van Noorden, C. J. F. & Köhler, A. Short-time induction of oxidative stress in hepatocytes of the European flounder (Platichthys flesus). Mar. Environ. Res. 50, 495–501 (2000). 282. Antonini, J. M. et al. Application of laser scanning confocal microscopy in the analysis of particle-induced pulmonary fibrosis. Toxicol. Sci. 51, 126–134 (1999). 283. Barrouillet, M. P., Moiret, A. & Cambar, J. Protective effects of polyphenols against cadmium-induced glomerular mesangial cell myocontracture. Arch. Toxicol. 73, 485–488 (1999). 284. Smith Orris, A., Banicky, L. C. & Wiens, D. J. Isotretinoin alters morphology, polarity, and motility of neural crest cells in culture. Reprod. Toxicol. 13, 45–52 (1999). 285. Köhler, A. & Van Noorden, C. J. F. Initial velocities in situ of G6PDH and PGDH and expression of proliferating cell nuclear antigen (PCNA): sensitive diagnostic markers of environmentally induced hepatocellular carcinogenesis in a marine flatfish (Platichthys flesus L.). Aquat. Toxicol. 40, 233–252 (1998). 286. McDonald-Taylor, C. K., Singh, A. & Gilman, A. Uranyl Nitrate—Induced Proximal Tubule Alterations in Rabbits: A Quantitative Analysis. Toxicol. Pathol. 25, 381–389 (1997). 287. Wu, C., Fry, C. H. & Henry, J. A. Membrane toxicity of opioids measured by protozoan motility. Toxicology 117, 35–44 (1997). 288. Anderson, D., Yu, T.-W. & Schmezer, P. An investigation of the DNA-damaging ability of benzene and its metabolites in human lymphocytes, using the comet assay. Environ. Mol. Mutagen. 26, 305–314 (1995). 289. Harkema, J. R. & Hotchkiss, J. A. Ozone- and endotoxin-induced mucous cell metaplasias in rat airway epithelium: Novel animal models to study toxicant-induced epithelial transformation in airways. Toxicol. Lett. 68, 251–263 (1993). 290. Luthman, J. et al. Combined lead acetate and disulfiram treatment-induced alterations of glial fibrillary acidic protein (GFA) immunoreactive astrocytes in brain smears. Toxicology 65, 333–346 (1991). 291. Andrade, C. Types of Analysis: Planned (prespecified) vs Post Hoc, Primary vs Secondary, Hypothesis-driven vs Exploratory, Subgroup and Sensitivity, and Others. Indian J. Psychol. Med. 45, 640–641 (2023). 292. Alfaro, J. M., Ripoll-Gómez, J. & Burgos, J. S. Kainate administered to adult zebrafish causes seizures similar to those in rodent models. Eur. J. Neurosci. 33, 1252–1255 (2011). 293. Li, Y. et al. Novel functional properties of Drosophila CNS glutamate receptors. Neuron 92, 1036–1048 (2016). 294. Polinski, J. M., Kron, N., Smith, D. R. & Bodnar, A. G. Unique age-related transcriptional signature in the nervous system of the long-lived red sea urchin Mesocentrotus franciscanus. Sci. Rep. 10, 9182 (2020). 295. Onakpoya, I., Heneghan, C. & Aronson, J. Worldwide withdrawal of medicinal products because of adverse drug reactions: a systematic review and analysis. Crit. Rev. Toxicol. 46, 1–13 (2016). 296. Serag, A. et al. Translational AI and Deep Learning in Diagnostic Pathology. Front. Med. 6, (2019). 297. Suarez-Arnedo, A. et al. An image J plugin for the high throughput image analysis of ин витро scratch wound healing assays. PLoS ONE 15, e0232565 (2020). 298. Nürnberg, E. et al. Routine Optical Clearing of 3D-Cell Cultures: Simplicity Forward. Front. Mol. Biosci. 7, (2020). 299. von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat. Commun. 12, 2276 (2021). 300. Klein, C. J. M. I. et al. Measuring Locomotor Activity and Behavioral Aspects of Rodents Living in the Home-Cage. Front. Behav. Neurosci. 16, 877323 (2022). 301. Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016). 302. Sinaci, A. A. et al. From Raw Data to FAIR Data: The FAIRification Workflow for Health Research. Methods Inf. Med. 59, e21–e32 (2020). 303. Kemmer, I. et al. Building a FAIR image data ecosystem for microscopy communities. Histochem. Cell Biol. 160, 199–209 (2023). 304. Turner, O. C. et al. Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology. Toxicol. Pathol. 48, 277–294 (2020). 305. von Krogh, G. & Spaeth, S. The open source software phenomenon: Characteristics that promote research. J. Strateg. Inf. Syst. 16, 236–253 (2007). 306. Tinevez, J.-Y. et al. TrackMate: An open and extensible platform for single-particle tracking. Methods 115, 80–90 (2017). 307. Sage, D. et al. DeconvolutionLab2: An open-source software for deconvolution microscopy. Methods 115, 28–41 (2017). 308. Gyori, B. M., Venkatachalam, G., Thiagarajan, P. S., Hsu, D. & Clement, M.-V. OpenComet: An automated tool for comet assay image analysis. Redox Biol. 2, 457–465 (2014). 309. Tischer, C., Schindelin, J., Rueden, C. & Hiner, M. Linear Kuwahara Filter Plugin. (2017). 310. Varghese, F., Bukhari, A. B., Malhotra, R. & De, A. IHC Profiler: An Open Source Plugin for the Quantitative Evaluation and Automated Scoring of Immunohistochemistry Images of Human Tissue Samples. PLoS ONE 9, e96801 (2014). 311. Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006). 312. Jones, T. R. et al. CellProfiler Analyst: data exploration and analysis software for complex image-based screens. BMC Bioinformatics 9, 482 (2008). 313. Merkel, D. & others. Docker: lightweight linux containers for consistent development and deployment. Linux J 239, 2 (2014). 314. Cito, J. et al. An Empirical Analysis of the Docker Container Ecosystem on GitHub. in 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR) 323–333 (2017). doi:10.1109/MSR.2017.67. 315. de Chaumont, F. et al. Icy: an open bioimage informatics platform for extended reproducible research. Nat. Methods 9, 690–696 (2012). 316. Van Rossum, G. & Drake Jr, F. L. Python Tutorial. vol. 620 (Centrum voor Wiskunde en Informatica Amsterdam, The Netherlands, 1995). 317. Rossum, G. V. Python Software Foundation. Python Language Reference, Version 2.7. (1995). 318. The Galaxy Community. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update. Nucleic Acids Res. 50, W345–W351 (2022). 319. Berthold, M. R. et al. KNIME - the Konstanz Information Miner: Version 2.0 and Beyond. SIGKDD Explor Newsl 11, 26–31 (2009). 320. Pfeuffer, A. F. C. D. J. KNIME for reproducible cross-domain analysis of life. J. Proteome Res. 7, 3354–63 (2008). 321. Bitter, R., Mohiuddin, T. & Nawrocki, M. LabVIEW: Advanced Programming Techniques. (CRC press, 2017). 322. Mir, S. B. & Llueca, G. F. Introduction to Programming Using Mobile Phones and MIT App Inventor. IEEE Rev. Iberoam. Tecnol. Aprendiz. 15, 192–201 (2020). 323. Dietz, C. et al. Integration of the ImageJ Ecosystem in KNIME Analytics Platform. Front. Comput. Sci. 2, (2020). 324. Lu, M. Y. et al. A Foundational Multimodal Vision Language AI Assistant for Human Pathology. Preprint at https://doi.org/10.48550/arXiv.2312.07814 (2023). 325. Haase, R. et al. A Hitchhiker's guide through the bio-image analysis software universe. FEBS Lett. 596, 2472–2485 (2022). 326. Rueden, C et al. ImageJ Ops. (2021). 327. Sommer, C., Straehle, C., Kothe, U. & Hamprecht, F. A. Ilastik: Interactive learning and segmentation toolkit. in 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 230–233 (IEEE, Chicago, IL, USA, 2011). doi:10.1109/ISBI.2011.5872394. 328. Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018). 329. Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W. & Smith, S. M. FSL. NeuroImage 62, 782–790 (2012). 330. Eaton, J. W., Bateman, D., Hauberg, S. & Wehbring, R. GNU Octave Version 5.2.0 Manual: A High-Level Interactive Language for Numerical Computations. (2020). 331. Stallman, R. & others. The GNU manifesto. (1985). 332. Carnë Draug, Hartmut Gimpel, & Avinoam Kalma. Image package. (2022). 333. napari: a multi-dimensional image viewer for Python. doi:10.5281/zenodo.8115575. 334. Jia, L. et al. Development of interactive biological web applications with R/Shiny. Brief. Bioinform. 23, bbab415 (2022). 335. Ooms, J. magick: advanced graphics and image-processing in R. CRAN R Package Version 1, (2018). 336. Austenfeld, M. & Beyschlag, W. A Graphical User Interface for R in a Rich Client Platform for Ecological Modeling. J. Stat. Softw. 49, 1–19 (2012). 337. Contributors, N. Napari: a fast, interactive, multi-dimensional image viewer for python. (2019). 338. Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020). 339. NumPy - Case Study: First Image of a Black Hole. https://numpy.org/case-studies/blackhole-image/. 340. LIGO Scientific Collaboration and Virgo Collaboration et al. Observation of Gravitational Waves from a Binary Black Hole Merger. Phys. Rev. Lett. 116, 061102 (2016). 341. NumPy - Case Study: Discovery of Gravitational Waves. https://numpy.org/case-studies/gw-discov/. 342. Chael, A. A. et al. HIGH-RESOLUTION LINEAR POLARIMETRIC IMAGING FOR THE EVENT HORIZON ℡ESCOPE. Astrophys. J. 829, 11 (2016). 343. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020). 344. Walt, S. van der et al. scikit-image: image processing in Python. PeerJ 2, e453 (2014). 345. Berman, A. G., Orchard, W. R., Gehrung, M. & Markowetz, F. PathML: A unified framework for whole-slide image analysis with deep learning. 2021.07.07.21260138 Preprint at https://doi.org/10.1101/2021.07.07.21260138 (2021). 346. Brahmbhatt, S. Practical OpenCV. (Apress, 2013). 347. Hangün, B. & Eyeci̇oğlu, Ö. Performance Comparison Between OpenCV Built in CPU and GPU Functions on Image Processing Operations. Int. J. Eng. Sci. Appl. 1, 34–41 (2017). 348. Pulli, K., Baksheev, A., Kornyakov, K. & Eruhimov, V. Real-time computer vision with OpenCV. Commun. ACM 55, 61–69 (2012). 349. Wink, S., Burger, G. A., Dévédec, S. E. L., Beltman, J. B. & Water, B. van de. User-friendly high-content imaging analysis on a single desktop: R package H5CellProfiler. 2022.10.06.511212 Preprint at https://doi.org/10.1101/2022.10.06.511212 (2022). 350. Marques, O. Practical Image and Video Processing Using MATLAB. (John Wiley & Sons, 2011). 351. Pajankar, A. & Chandu, S. Image and Video Processing. in GNU Octave by Example: A Fast and Practical Approach to Learning GNU Octave (eds. Pajankar, A. & Chandu, S.) 147–161 (Apress, Berkeley, CA, 2020). doi:10.1007/978-1-4842-6086-9_9. 352. Stritt, M., Stalder, A. K. & Vezzali, E. Orbit Image Analysis: An open-source whole slide image analysis tool. PLOS Comput. Biol. 16, e1007313 (2020). 353. Marée, R. et al. Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics 32, 1395–1401 (2016). 354. Miura, K. et al. Bioimage Analysis Tools. (2020). 355. Allan, C. et al. OMERO: flexible, model-driven data management for experimental biology. Nat. Methods 9, 245–253 (2012). 356. Edelstein, A., Amodaj, N., Hoover, K., Vale, R. & Stuurman, N. Computer Control of Microscopes Using µManager. Curr. Protoc. Mol. Biol. 92, 14.20.1-14.20.17 (2010). 357. Edelstein, A. D. et al. Advanced methods of microscope control using μManager software. J. Biol. Methods 1, e10 (2014). 358. Bauch, A. et al. OpenBIS: A flexible framework for managing and analyzing complex data in biology research. BMC Bioinformatics 12, 468 (2011). 359. Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications | IEEE Journals & Magazine | IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/8485684. 360. Ouyang, W., Mueller, F., Hjelmare, M., Lundberg, E. & Zimmer, C. ImJoy: an open-source computational platform for the deep learning era. Nat. Methods 16, 1199–1200 (2019). 361. Haberl, M. G. et al. CDeep3M - Plug-and-Play cloud based deep learning for image segmentation of light, electron and X-ray microscopy. 353425 Preprint at https://doi.org/10.1101/353425 (2018). 362. Gómez-de-Mariscal, E. et al. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nat. Methods 18, 1192–1195 (2021). 363. Stevens, M. et al. StarDist Image Segmentation Improves Circulating Tumor Cell Detection. Cancers 14, 2916 (2022). 364. Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell Detection with Star-Convex Polygons. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (eds. Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-López, C. & Fichtinger, G.) 265–273 (Springer International Publishing, Cham, 2018). doi:10.1007/978-3-030-00934-2_30. 365. Jacquemet, G. Deep learning to analyse microscopy images. The Biochemist 43, 60–64 (2021). 366. Nath, T. et al. Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nat. Protoc. 14, 2152–2176 (2019). 367. Deschamps, J., Dalle Nogare, D. & Jug, F. Better research software tools to elevate the rate of scientific discovery or why we need to invest in research software engineering. Front. Bioinforma. 3, 1255159 (2023). 368. What's next for bioimage analysis? Nat. Methods 20, 945–946 (2023). 369. Yordanov, Y. I. Hep G2 cell culture confluence measurement in phase-contrast micrographs – a user-friendly, open-source software-based approach. Toxicol. Mech. Methods 30, 146–152 (2020). 370. Bruggisser, R., von Daeniken, K., Jundt, G., Schaffner, W. & Tullberg-Reinert, H. Interference of plant extracts, phytoestrogens and antioxidants with the MTT tetrazolium assay. Planta Med. 68, 445–448 (2002). 371. Langdon, S. P. Cancer Cell Culture: Methods and Protocols. (Humana Press, Totowa, N.J, 2004). 372. Karakaş, D., Ari, F. & Ulukaya, E. The MTT viability assay yields strikingly false-positive viabilities although the cells are killed by some plant extracts. Turk. J. Biol. Turk Biyol. Derg. 41, 919–925 (2017). 373. Sardão, V. A., Oliveira, P. J., Holy, J., Oliveira, C. R. & Wallace, K. B. Vital imaging of H9c2 myoblasts exposed to tert-butylhydroperoxide – characterization of morphological features of cell death. BMC Cell Biol. 8, 11 (2007). 374. Helmy, I. M. & Abdel Azim, A. M. Efficacy of ImageJ in the assessment of apoptosis. Diagn. Pathol. 7, 15 (2012). 375. Povea-Cabello, S. et al. Dynamic Reorganization of the Cytoskeleton during Apoptosis: The Two Coffins Hypothesis. Int. J. Mol. Sci. 18, (2017). 376. Kalinova, R. et al. Cinnamyl modified polymer micelles as efficient carriers of caffeic acid phenethyl ester. React. Funct. Polym. 157, 104763 (2020). 377. Groebe, K. et al. Unexpected common mechanistic pathways for embryotoxicity of warfarin and lovastatin. Reprod. Toxicol. 30, 121–130 (2010). 378. Mackay, C. R. Moving targets: cell migration inhibitors as new anti-inflammatory therapies. Nat. Immunol. 9, 988–998 (2008). 379. Tsai, W.-C. et al. Ciprofloxacin-mediated inhibition of tenocyte migration and down-regulation of focal adhesion kinase phosphorylation. Eur. J. Pharmacol. 607, 23–26 (2009). 380. Zhao, X.-K. et al. Focal Adhesion Kinase Regulates Fibroblast Migration via Integrin beta-1 and Plays a Central Role in Fibrosis. Sci. Rep. 6, 19276 (2016). 381. Jiang, D. et al. Injury triggers fascia fibroblast collective cell migration to drive scar formation through N-cadherin. Nat. Commun. 11, 5653 (2020). 382. Knoedler, S. et al. Fibroblasts – the cellular choreographers of wound healing. Front. Immunol. 14, 1233800 (2023). 383. Stuelten, C. H., Parent, C. A. & Montell, D. J. Cell motility in cancer invasion and metastasis: insights from simple model organisms. Nat. Rev. Cancer 18, 296–312 (2018). 384. Mu, H. et al. Low doses of deoxynivalenol inhibit the cell migration mediated by H3K27me3-targeted downregulation of TEM8 expression. Biochem. Pharmacol. 175, 113897 (2020). 385. Egbowon, B. F., Harris, W., Arnott, G., Mills, C. L. & Hargreaves, A. J. Sub-lethal concentrations of CdCl2 disrupt cell migration and cytoskeletal proteins in cultured mouse TM4 Sertoli cells. Toxicol. Ин витро 32, 154–165 (2016). 386. Devreotes, P. & Horwitz, A. R. Signaling Networks that Regulate Cell Migration. Cold Spring Harb. Perspect. Biol. 7, a005959 (2015). 387. Jonkman, J. E. N. et al. An introduction to the wound healing assay using live-cell microscopy. Cell Adhes. Migr. 8, 440–451 (2014). 388. Baecker, V. ImageJ Macro Tool Sets for Biological Image Analysis. (2012) doi:10.13140/RG.2.1.2868.9446. 389. Końca, K. et al. A cross-platform public domain PC image-analysis program for the comet assay. Mutat. Res. Toxicol. Environ. Mutagen. 534, 15–20 (2003). 390. Kopp, B., Khoury, L. & Audebert, M. Validation of the γH2AX biomarker for genotoxicity assessment: a review. Arch. Toxicol. 93, 2103–2114 (2019). 391. Cordelli, E., Bignami, M. & Pacchierotti, F. Comet assay: a versatile but complex tool in genotoxicity testing. Toxicol. Res. 10, 68–78 (2021). 392. Hartmann, A. et al. Recommendations for conducting the ин виво alkaline Comet assay. Mutagenesis 18, 45–51 (2003). 393. Singer, V. L., Lawlor, T. E. & Yue, S. Comparison of SYBR® Green I nucleic acid gel stain mutagenicity and ethidium bromide mutagenicity in the Salmonella/mammalian microsome reverse mutation assay (Ames test). Mutat. Res. Toxicol. Environ. Mutagen. 439, 37–47 (1999). 394. Miyamae, Y., Zaizen, K., Ohara, K., Mine, Y. & Sasaki, Y. F. Detection of DNA lesions induced by chemical mutagens by the single cell gel electrophoresis (Comet) assay. 1. Relationship between the onset of DNA damage and the characteristics of mutagens. Mutat. Res. 393, 99–106 (1997). 395. Kumaravel, T. S., Vilhar, B., Faux, S. P. & Jha, A. N. Comet Assay measurements: a perspective. Cell Biol. Toxicol. 25, 53–64 (2009). 396. Georgiev, G. P. et al. A comparative study of the epiligament of the medial collateral and anterior cruciate ligaments in the human knee: Immunohistochemical analysis of CD 34, α-smooth muscle actin and vascular endothelial growth factor in relation to epiligament theory. The Knee 39, 78–90 (2022). 397. Niazi, M. K. K., Parwani, A. V. & Gurcan, M. N. Digital pathology and artificial intelligence. Lancet Oncol. 20, e253–e261 (2019). 398. Zarella, M. D. et al. A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association. Arch. Pathol. Lab. Med. 143, 222–234 (2019). 399. Siwczak, F., Hiller, C., Pfannkuche, H. & Schneider, M. R. Culture of vibrating microtome tissue slices as a 3D model in biomedical research. J. Biol. Eng. 17, 36 (2023). 400. Georgiev, G. P. et al. A comparative study of the epiligament of the medial collateral and the anterior cruciate ligament in the human knee. Immunohistochemical analysis of collagen type I and V and procollagen type III. Ann. Anat. - Anat. Anz. 224, 88–96 (2019). 401. Yoncheva, K. et al. Encapsulation of doxorubicin in chitosan-alginate nanoparticles improves its stability and cytotoxicity in resistant lymphoma L5178 MDR cells. J. Drug Deliv. Sci. Technol. 59, 101870 (2020). 402. Motlagh, N. S. H., Parvin, P., Ghasemi, F. & Atyabi, F. Fluorescence properties of several chemotherapy drugs: doxorubicin, paclitaxel and bleomycin. Biomed. Opt. Express 7, 2400–2406 (2016). 403. Berlin, G. & Enerbäck, L. Fluorescent Berberine Binding as a Marker of Secretory Activity in Mast Cells. Int. Arch. Allergy Appl. Immunol. 71, 332–339 (2009). 404. Matsson, P., Pedersen, J. M., Norinder, U., Bergström, C. A. S. & Artursson, P. Identification of novel specific and general inhibitors of the three major human ATP-binding cassette transporters P-gp, BCRP and MRP2 among registered drugs. Pharm. Res. 26, 1816–1831 (2009). 405. Schneckenburger, H. et al. Light exposure and cell viability in fluorescence microscopy. J. Microsc. 245, 311–318 (2012).

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