Large-Scale Annotation Dataset for Fetal Head Biometry in Ultrasound Images
Creators
- 1. HBKU
- 2. Sidra
- 3. UDST
Description
Dataset Overview
Title: Large-Scale Annotation Dataset for Fetal Head Biometry in Ultrasound Images
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Total Images: 3,832
Image Dimensions: 959 x 661 pixels
Description: This dataset provides a comprehensive collection of ultrasound images focusing on fetal head biometry. It is designed to support the development and evaluation of image segmentation and biometric analysis algorithms in prenatal diagnostics. The images have been carefully annotated by experts in the field, ensuring high-quality data for researchers.
For additional details, including data structure, annotation guidelines, and access instructions, please refer to the readme.txt file included in the dataset package.
Citation Instructions: If you utilize this dataset in your research, please acknowledge the work of the contributors by citing the following papers:
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Dataset Paper:
Mahmood Alzubaidi, Marco Agus, Michel Makhlouf, Fatima Anver, Khalid Alyafei, Mowafa Househ, "Large-Scale Annotation Dataset for Fetal Head Biometry in Ultrasound Images," Data in Brief, 2023, 109708, ISSN 2352-3409.
DOI: https://doi.org/10.1016/j.dib.2023.109708 -
Baseline Paper:
M. Alzubaidi, U. Shah, M. Agus, and M. Househ, "FetSAM: Advanced Segmentation Techniques for Fetal Head Biometrics in Ultrasound Imagery," IEEE Open Journal of Engineering in Medicine and Biology.
DOI: https://ieeexplore.ieee.org/document/10480532 - Review Paper: Alzubaidi, M., Agus, M., Alyafei, K., Althelaya, K.A., Shah, U., Abd-Alrazaq, A., Anbar, M., Makhlouf, M. and Househ, M., 2022. Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images. Iscience. DOI: https://doi.org/10.48550/arXiv.2201.07935
- How Pixel is Converted Into Millimeter: Alzubaidi, M., Shah, U., Shah, H. and Househ, M., 2023, August. Conversion of Pixel to Millimeter in Ultrasound Images: A Methodological Approach and Dataset. In 2023 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp. 1-6). IEEE. DOI: https://ieeexplore.ieee.org/document/10264909
Keywords: Ultrasonic imaging, Image segmentation, Head, Brain modeling, Biological system modeling, Imaging, Ultrasonic variables measurement, Fetal Ultrasound Imaging, Image Segmentation, Prompt-based Learning, Prenatal Diagnostics, Ultrasound Biometrics.
Files
Diverse Fetal Head Images-orginal-image.zip
Files
(2.4 GB)
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Additional details
References
- @article{ALZUBAIDI2023109708, title = {Large-Scale Annotation Dataset for Fetal Head Biometry in Ultrasound Images}, journal = {Data in Brief}, pages = {109708}, year = {2023}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2023.109708}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923007837}, author = {Mahmood Alzubaidi and Marco Agus and Michel Makhlouf and Fatima Anver and Khalid Alyafei and Mowafa Househ}, keywords = {Fetal ultrasound imaging, Computer vision, Data annotation, Medical imaging}, abstract = {This dataset features a collection of 3,832 high-resolution ultrasound images, each with dimensions of 959×661 pixels, focused on Fetal heads. The images highlight specific anatomical regions: the brain, cavum septum pellucidum (CSP), and lateral ventricles (LV). The dataset was assembled under the Creative Commons Attribution 4.0 International license, using previously anonymized and de-identified images to maintain ethical standards. Each image is complemented by a CSV file detailing pixel size in millimeters (mm). For enhanced compatibility and usability, the dataset is available in 11 universally accepted formats, including Cityscapes, YOLO, CVAT, Datumaro, COCO, TFRecord, PASCAL, LabelMe, Segmentation mask, OpenImage, and ICDAR. This broad range of formats ensures adaptability for various computer vision tasks, such as classification, segmentation, and object detection. It is also compatible with multiple medical imaging software and deep learning frameworks. The reliability of the annotations is verified through a two-step validation process involving a Senior Attending Physician and a Radiologic Technologist. The Intraclass Correlation Coefficients (ICC) and Jaccard similarity indices (JS) are utilized to quantify inter-rater agreement. The dataset exhibits high annotation reliability, with ICC values averaging at 0.859 and 0.889, and JS values at 0.855 and 0.857 in two iterative rounds of annotation. This dataset is designed to be an invaluable resource for ongoing and future research projects in medical imaging and computer vision. It is particularly suited for applications in prenatal diagnostics, clinical diagnosis, and computer-assisted interventions. Its detailed annotations, broad compatibility, and ethical compliance make it a highly reusable and adaptable tool for the development of algorithms aimed at improving maternal and Fetal health.} }
- @ARTICLE{10480532, author={Alzubaidi, Mahmood and Shah, Uzair and Agus, Marco and Househ, Mowafa}, journal={IEEE Open Journal of Engineering in Medicine and Biology}, title={FetSAM: Advanced Segmentation Techniques for Fetal Head Biometrics in Ultrasound Imagery}, year={2024}, volume={}, number={}, pages={1-16}, keywords={Ultrasonic imaging;Image segmentation;Head;Brain modeling;Biological system modeling;Imaging;Ultrasonic variables measurement;Fetal Ultrasound Imaging;Image Segmentation;Prompt-based Learning;Prenatal Diagnostics;Ultrasound Biometrics}, doi={10.1109/OJEMB.2024.3382487}}