Author, as appears in the article.: Hassan, Loay; Abdel-Nasser, Mohamed; Saleh, Adel; 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 Deep learning Choquet integral
Abstract: 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.
Thematic Areas: 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
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: 2024-07-27
Journal volume: 10
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.mdpi.com/2079-9292/10/8/954
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Electronics. 10 (8):
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
Article's DOI: 10.3390/electronics10080954
Entity: Universitat Rovira i Virgili
Journal publication year: 2021
Publication Type: Journal Publications
Repositori URV