Abstract
Nuclei semantic segmentation is a key component for advancing machine learning and deep learning applications in digital pathology. However, most existing segmentation models are trained and tested on high-quality data acquired with expensive equipment, such as whole slide scanners, which are not accessible to most pathologists in developing countries. These pathologists rely on low-resource data acquired with low-precision microscopes, smartphones, or digital cameras, which have different characteristics and challenges than high-resource data. Therefore, there is a gap between the state-of-the-art segmentation models and the real-world needs of low-resource settings. This work aims to bridge this gap by presenting the first fully annotated African multi-organ dataset for histopathology nuclei semantic segmentation acquired with a low-precision microscope. We also evaluate state-of-the-art segmentation models, including spectral feature extraction encoder and vision transformer-based models, and stain normalization techniques for color normalization of Hematoxylin and Eosin-stained histopathology slides. Our results provide important insights for future research on nuclei histopathology segmentation with low-resource data. Code and dataset: https://github.com/zerouaoui/AMONUSEG.
H. Zerouaoui, O. Gbenga and R. Lefdali—Equal Contribution.
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Notes
- 1.
250x is calculated by the magnification of the eyepiece which is 10x and the objective lens which is 25x.
- 2.
Due to disagreements among pathologists [19], in this paper, we present annotations validated by three expert pathologists where they reached agreements.
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Acknowledgments
We would like to thank the following experts for validating the final annotation masks generated by annotators A1 & A2: Dr. Anis Hasnaoui, Assistant Professor at the Faculty of Medicine of Tunis, Prof. Kun-Hsing Yu, Assistant Professor of Biomedical Informatics at Harvard Medical School, and Dr. Amal Fadaili, chief of pathology at Amana laboratory, Morocco.
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Zerouaoui, H. et al. (2024). AMONuSeg: A Histological Dataset for African Multi-organ Nuclei Semantic Segmentation. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15009. Springer, Cham. https://doi.org/10.1007/978-3-031-72114-4_10
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