Published May 25, 2021 | Version v1.0
Dataset Open

Pathologist's Annotated Image Tiles for Multi-Class Tissue Classification in Colorectal Cancer

Authors/Creators

  • 1. Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy
  • 2. Molecular Diagnostics and Pharmacogenetics Unit, IRCCS Istituto Tumori Giovanni Paolo II, Viale Orazio Flacco 65, Bari, Italy
  • 3. Pathology Department, IRCCS Istituto Tumori Giovanni Paolo II, Viale Orazio Flacco 65, Bari, Italy

Description

Content

The present dataset is related to a study aiming to identify the best method to perform multi-tissue classification from digital histological images. Histological images, completely anomized, come from  formalin-fized paraffine-embedded sample of a patient affected by colorectal cancer.

Two directories are available:

  1. “CRC_image_tiles.zip”: a zipped folder containing tiles (n=5984) annotated by a pathologist, grouped in 7 subdirectories, each of them representing a class ( 150 * 150 px).
  2. “Macenko_normalized_CRC_image_tiles.zip”: Macenko-normalized tiles (n=5984) annotated by a pathologist, grouped in 7 subdirectories, each of them representing a class ( 150 * 150 px).

 

Ethical Statement

The study has been funded by “Tecnopolo per la Medicina di Precisione (CUP B84I18000540002)”. The institutional Ethic Committee approved the study (Prot n. 780/CE).

 

Info and Data Usage 

For further details concerning the aforementioned dataset, refer to the paper below. Please cite the following articles if you need this dataset for your research.

Altini N. et al. (2021) Multi-class Tissue Classification in Colorectal Cancer with Handcrafted and Deep Features. In: Huang DS., Jo KH., Li J., Gribova V., Bevilacqua V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science, vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_42

Altini, N., Marvulli, T. M., Zito, F. A., Caputo, M., Tommasi, S., Azzariti, A., ... & Bevilacqua, V. (2023). The Role of Unpaired Image-to-Image Translation for Stain Color Normalization in Colorectal Cancer Histology Classification. Computer Methods and Programs in Biomedicine, 107511. 
https://doi.org/10.1016/j.cmpb.2023.107511

 

Files

CRC_image_tiles.zip

Files (612.4 MB)

Name Size Download all
md5:aebdf9d3fdde660ffb0bee302923f644
289.1 MB Preview Download
md5:e25c98b7112ebb3b58e4e1c5348419d5
323.3 MB Preview Download

Additional details

References

  • M. Macenko et al., "A method for normalizing histology slides for quantitative analysis," 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009, pp. 1107-1110, doi: 10.1109/ISBI.2009.5193250.