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Efficient Multi-Organ Multi-Center Cell Nuclei Segmentation Method Based on Deep Learnable Aggregation Network

  • Dades identificatives

    Identificador: imarina:9226402
    Autors:
    Hassan, LoaySaleh, AdelAbdel-Nasser, MohamedOmer, Osama APuig, Domenec
    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.
  • Altres:

    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
  • Paraules clau:

    Computer Science, Artificial Intelligence,Electrical and Electronic Engineering,Engineering, Electrical & Electronic
    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
    Engineering, electrical & electronic
    Electrical and electronic engineering
    Computer science, artificial intelligence
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