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Maximising Histopathology Segmentation using Minimal Labels via Self-Supervision

Histopathology, the microscopic examination of tissue samples, is essential for disease diagnosis and prognosis. Accurate segmentation and identification of key regions in histopathology images are crucial for developing automated solutions. However, state-of-art deep learning segmentation methods like UNet require extensive labels, which is both costly and time-consuming, particularly when dealing with multiple stainings. To mitigate this, multi-stain segmentation methods such as MDS1 and UDAGAN have been developed, which reduce the need for labels by requiring only one (source) stain to be labelled. Nonetheless, obtaining source stain labels can still be challenging, and segmentation models fail when they are unavailable. This article shows that through self-supervised pre-training, including SimCLR, BYOL, and a novel approach, HR-CS-CO, the performance of these segmentation methods (UNet, MDS1, and UDAGAN) can be retained even with 95% fewer labels. Notably, with self-supervised pre-training and using only 5% labels, the performance drops are minimal: 5.9% for UNet, 4.5% for MDS1, and 6.2% for UDAGAN, compared to their respective fully supervised counterparts (without pre-training, using 100% labels). The code is available from https://github.com/zeeshannisar/improve_kidney_glomeruli_segmentation [to be made public upon acceptance].

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