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: 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
Abstract: 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.
Thematic Areas: Engineering, electrical & electronic Electrical and electronic engineering Computer science, 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: 2024-10-12
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: Trait Signal. 38 (3): 653-661
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
Article's DOI: 10.18280/ts.380312
Entity: Universitat Rovira i Virgili
Journal publication year: 2021
Publication Type: Journal Publications