An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images

A key step in computational pathology is to automate the laborious process of manual nuclei segmentation in Hematoxylin and Eosin (H&E) stained whole slide images (WSIs). Despite lots of efforts put forward by the researchers to develop automated nuclei segmentation methodologies in the literature, the segmentation performance is still constrained due to several challenges, including overlapping and clumped nuclei, scanners with different resolutions and nuclei with varying sizes and shapes. In this paper, we introduce an imbalance -aware nuclei segmentation methodology to deal with class imbalance problems in H&E stained histopathology images. The introduced methodology involves the following improvements: (1) the design of a preprocessing stage with a variety of resize-split, augmentation and normalization techniques, and (2) an enhanced lightweight U-Net architecture with a generalized Dice loss layer. To prove its effectiveness and efficiency, a comprehensive experimental study is carried out on a well-known benchmark, namely the MonuSeg2018 dataset. According to the results, the proposed methodology outperforms various recently introduced studies in terms of well-known evaluation metrics, such as Aggregated Jaccard Index (AJI) and Intersection of Union (IoU).

Automated summary

A key step in computational pathology is to automate the manual nuclei segmentation process in H&E stained whole slide images. This paper introduces an imbalance-aware nuclei segmentation methodology that outperforms recent studies in terms of evaluation metrics on histopathology images.