Efficient Staining-Invariant Nuclei Segmentation Approach Using Self-Supervised Deep Contrastive Network

Autor: Mohamed Abdel-Nasser, Vivek Kumar Singh, Ehab Mahmoud Mohamed
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Diagnostics, Vol 12, Iss 12, p 3024 (2022)
Druh dokumentu: article
ISSN: 2075-4418
20385544
DOI: 10.3390/diagnostics12123024
Popis: Existing nuclei segmentation methods face challenges with hematoxylin and eosin (H&E) whole slide imaging (WSI) due to the variations in staining methods and nuclei shapes and sizes. Most existing approaches require a stain normalization step that may cause losing source information and fail to handle the inter-scanner feature instability problem. To mitigate these issues, this article proposes an efficient staining-invariant nuclei segmentation method based on self-supervised contrastive learning and an effective weighted hybrid dilated convolution (WHDC) block. In particular, we propose a staining-invariant encoder (SIE) that includes convolution and transformers blocks. We also propose the WHDC block allowing the network to learn multi-scale nuclei-relevant features to handle the variation in the sizes and shapes of nuclei. The SIE network is trained on five unlabeled WSIs datasets using self-supervised contrastive learning and then used as a backbone for the downstream nuclei segmentation network. Our method outperforms existing approaches in challenging multiple WSI datasets without stain color normalization.
Databáze: Directory of Open Access Journals
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