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AMONuSeg: A Histological Dataset for African Multi-Organ Nuclei Semantic Segmentation

MCML Authors

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.

inproceedings ZOL+24

MICCAI 2024

27th International Conference on Medical Image Computing and Computer Assisted Intervention. Marrakesh, Morocco, Oct 06-10, 2024.
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Authors

H. Zerouaoui • G. P. Oderinde • R. Lefdali • K. Echihabi • S. P. Akpulu • N. A. Agbon • A. S. Musa • Y. Yeganeh • A. Farshad • N. Navab

Links

DOI GitHub

Research Area

 C1 | Medicine

BibTeXKey: ZOL+24

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