Author, as appears in the article.: Abdel-Nasser, Mohamed; Singh, Vivek Kumar; Mohamed, Ehab Mahmoud
Department: Enginyeria Informàtica i Matemàtiques
URV's Author/s: Abdelnasser Mohamed Mahmoud, Mohamed
Keywords: Whole slide imaging Stain color normalization Nuclei segmentation Images Hematoxylin and eosin (h&e) Hematoxylin and eosin (h&e) Hematoxylin and eosin (h&e) Deep learning
Abstract: 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.
Thematic Areas: Medicine, general & internal Internal medicine Clinical biochemistry
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: mohamed.abdelnasser@urv.cat
Author identifier: 0000-0002-1074-2441
Record's date: 2024-10-12
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.mdpi.com/2075-4418/12/12/3024
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Diagnostics. 12 (12): 3024-
APA: Abdel-Nasser, Mohamed; Singh, Vivek Kumar; Mohamed, Ehab Mahmoud (2022). Efficient Staining-Invariant Nuclei Segmentation Approach Using Self-Supervised Deep Contrastive Network. Diagnostics, 12(12), 3024-. DOI: 10.3390/diagnostics12123024
Article's DOI: 10.3390/diagnostics12123024
Entity: Universitat Rovira i Virgili
Journal publication year: 2022
Publication Type: Journal Publications