Autor segons l'article: Hassan, Loay; Saleh, Adel; Abdel-Nasser, Mohamed; Omer, Osama A; Puig, Domenec
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
Paraules clau: Wsi circuits Whole slide imaging (wsi) Whole slide imaging Rough segmentation Nuclei segmentation Learning systems Individual cells Image segmentation Graphical user interfaces Grading Diseases Digital pathology Deep learning Cytology Computer-aided diagnosis Competitive performance Color variations Cells Cell nuclei segmentation Aggregation network
Resum: Automated cell nuclei delineation in whole-slide imaging (WSI) is a fundamental step for many tasks like cancer cell recognition, cancer grading, and cancer subtype classification. Although numerous computational methods have been proposed for segmenting nuclei in WSI images based on image processing and deep learning, existing approaches face major challenges such as color variation due to the use of different stains, the various structures of cell nuclei, and the overlapping and clumped cell nuclei. To circumvent these challenges in this article, we propose an efficient and accurate cell nuclei segmentation method based on deep learning, in which a set of accurate individual cell nuclei segmentation models are developed to predict rough segmentation masks, and then a learnable aggregation network (LANet) is used to predict the final nuclei masks. Besides, we develop cell nuclei segmentation software (with a graphical user interface_GUI) that includes the proposed method and other deep-learning-based cell nuclei segmentation methods. A challenging WSI dataset collected from different centers and organs is used to demonstrate the efficiency of our method. The experimental results reveal that our method obtains a competitive performance compared to the existing approaches in terms of the aggregated Jaccard index (A11=89.25%) and F1-score (F1=73.02%). The developed nuclei segmentation software can be downloaded from https://github.com/loaysh2010/Cell-Nuclei-Segmentation-GUI Application.
Àrees temàtiques: Engineering, electrical & electronic Electrical and electronic engineering Computer science, artificial intelligence
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat
Identificador de l'autor: 0000-0002-1074-2441 0000-0002-0562-4205
Data d'alta del registre: 2024-10-12
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.mdpi.com/2079-9292/10/8/954
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Trait Signal. 38 (3): 653-661
Referència de l'ítem segons les normes APA: Hassan, Loay; Saleh, Adel; Abdel-Nasser, Mohamed; Omer, Osama A; Puig, Domenec (2021). Efficient Multi-Organ Multi-Center Cell Nuclei Segmentation Method Based on Deep Learnable Aggregation Network. Trait Signal, 38(3), 653-661. DOI: 10.18280/ts.380312
DOI de l'article: 10.18280/ts.380312
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2021
Tipus de publicació: Journal Publications