HuBMAP + HPA: Functional Tissue Unit Segmentation

We have deveoped a model to segment cell population neighborhoods that perform an organs main function, also called as Functional Tissue Units (FTU) of 5 organs, namely: kidney, large intestine, lung, prostate and spleen. Here, we present a vision transformer-based approach using Coat (Co-Scale Conv-Attentional Image Transformers) architecture for binary FTU segmentation. We also implemented techniques like stain normalization, data augmentation and switched auxiliary loss for robust training which provided an improvement in accuracy of over 6%. Overall, our model achieved a dice score of 0.793 on the public dataset and 0.778 on the private dataset. The findings bolster the use of vision transformers-based models for dense prediction tasks. The study also assists in understanding the relationships between cell and tissue organization thereby providing a comprehensive understanding of how cellular functions impact human health.