Autor según el artículo: Abdel-Nasser, Mohamed; Singh, Vivek Kumar; Mohamed, Ehab Mahmoud;
Departamento: Enginyeria Informàtica i Matemàtiques
Autor/es de la URV: Abdelnasser Mohamed Mahmoud, Mohamed
Palabras clave: Whole slide imaging Stain color normalization Nuclei segmentation Images Hematoxylin and eosin (h&e) Deep learning
Resumen: 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.
Áreas temáticas: Medicine, general & internal Clinical biochemistry
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: mohamed.abdelnasser@urv.cat
Identificador del autor: 0000-0002-1074-2441
Fecha de alta del registro: 2023-05-20
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.mdpi.com/2075-4418/12/12/3024
Referencia al articulo segun fuente origial: Diagnostics. 12 (12):
Referencia de l'ítem segons les normes 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), -. DOI: 10.3390/diagnostics12123024
URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
DOI del artículo: 10.3390/diagnostics12123024
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2022
Tipo de publicación: Journal Publications
Repositori URV