An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images. (May 2023)

• Record Type: Journal Article
• Authors: Hancer, Emrah; Traoré, Mohamed; Samet, Refik; Yıldırım, Zeynep; Nemati, Nooshin
• Is Part Of: Biomedical signal processing and control. Volume 83(2023)
• Publication Date: 2023-05
• Subjects: Nuclei segmentation -- Computational pathology -- Semantic segmentation -- Generalized Dice loss; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2023.104720
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: 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). Highlights: We introduced an imbalance-aware nuclei segmentation methodology based on U-Net. We designed a preprocessing stage using resize-split, normalization and augmentation. We integrated a generalized Dice loss layer in the U-Net architecture. The proposed methodology performed better than a variety of recent works.