Author, as appears in the article.: Hassan, Loay; Saleh, Adel; Abdel-Nasser, Mohamed; Omer, Osama A.; Puig, Domenec;

Department: Enginyeria Informàtica i Matemàtiques

URV's Author/s: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi

Keywords: Whole slide imaging Nuclei segmentation Digital pathology Deep learning

Abstract: Nuclei segmentation in whole-slide imaging (WSI) plays a crucial role in the field of computational pathology. It is a fundamental task for different applications, such as cancer cell type classification, cancer grading, and cancer subtype classification. However, existing nuclei segmentation methods face many challenges, such as color variation in histopathological images, the overlapping and clumped nuclei, and the ambiguous boundary between different cell nuclei, that limit their performance. In this paper, we present promising deep semantic nuclei segmentation models for multi-institutional WSI images (i.e., collected from different scanners) of different organs. Specifically, we study the performance of pertinent deep learning-based models with nuclei segmentation in WSI images of different stains and various organs. We also propose a feasible deep learning nuclei segmentation model formed by combining robust deep learning architectures. A comprehensive comparative study with existing software and related methods in terms of different evaluation metrics and the number of parameters of each model, emphasizes the efficacy of the proposed nuclei segmentation models.

Thematic Areas: Statistics and probability Signal processing Linguística e literatura Interdisciplinar Engenharias iv Educação Computer vision and pattern recognition Computer science, interdisciplinary applications Computer science, artificial intelligence Computer science applications Computer networks and communications Ciência da computação Artificial intelligence

licence for use: https://creativecommons.org/licenses/by/3.0/es/

Author's mail: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat

Author identifier: 0000-0002-1074-2441 0000-0002-0562-4205

Record's date: 2023-05-14

Papper version: info:eu-repo/semantics/publishedVersion

Link to the original source: https://www.ijimai.org/journal/bibcite/reference/2827

Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/

Papper original source: International Journal Of Interactive Multimedia And Artificial Intelligence. 6 (6): 35-45

APA: Hassan, Loay; Saleh, Adel; Abdel-Nasser, Mohamed; Omer, Osama A.; Puig, Domenec; (2021). Promising Deep Semantic Nuclei Segmentation Models for Multi-Institutional Histopathology Images of Different Organs. International Journal Of Interactive Multimedia And Artificial Intelligence, 6(6), 35-45. DOI: 10.9781/ijimai.2020.10.004

Article's DOI: 10.9781/ijimai.2020.10.004

Entity: Universitat Rovira i Virgili

Journal publication year: 2021

Publication Type: Journal Publications