Autor según el artículo: Hassan, Loay; Abdel-Nasser, Mohamed; Saleh, Adel; Omer, Osama A.; Puig, Domenec;

Departamento: Enginyeria Informàtica i Matemàtiques

Autor/es de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi

Palabras clave: Whole-slide imaging Nuclei segmentation Deep learning Choquet integral

Resumen: Existing nuclei segmentation methods have obtained limited results with multi-center and multi-organ whole-slide images (WSIs) due to the use of different stains, scanners, overlapping, clumped nuclei, and the ambiguous boundary between adjacent cell nuclei. In an attempt to address these problems, we propose an efficient stain-aware nuclei segmentation method based on deep learning for multi-center WSIs. Unlike all related works that exploit a single-stain template from the dataset to normalize WSIs, we propose an efficient algorithm to select a set of stain templates based on stain clustering. Individual deep learning models are trained based on each stain template, and then, an aggregation function based on the Choquet integral is employed to combine the segmentation masks of the individual models. With a challenging multi-center multi-organ WSIs dataset, the experimental results demonstrate that the proposed method outperforms the state-of-art nuclei segmentation methods with aggregated Jaccard index (AJI) and F1-scores of 73.23% and 89.32%, respectively, while achieving a lower number of parameters.

Áreas temáticas: Signal processing Physics, applied Hardware and architecture Engineering, electrical & electronic Engenharias iv Electrical and electronic engineering Control and systems engineering Computer science, information systems Computer networks and communications

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

Direcció de correo del autor: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat

Identificador del autor: 0000-0002-1074-2441 0000-0002-0562-4205

Fecha de alta del registro: 2023-05-14

Volumen de revista: 10

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://www.mdpi.com/2079-9292/10/8/954

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Electronics. 10 (8):

Referencia de l'ítem segons les normes APA: Hassan, Loay; Abdel-Nasser, Mohamed; Saleh, Adel; Omer, Osama A.; Puig, Domenec; (2021). Efficient Stain-Aware Nuclei Segmentation Deep Learning Framework for Multi-Center Histopathological Images. Electronics, 10(8), -. DOI: 10.3390/electronics10080954

DOI del artículo: 10.3390/electronics10080954

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2021

Tipo de publicación: Journal Publications