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

  • Datos identificativos

    Identificador: imarina:9226402
    Autores:
    Hassan, LoaySaleh, AdelAbdel-Nasser, MohamedOmer, Osama APuig, Domenec
    Resumen:
    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.
  • Otros:

    Autor según el artículo: Hassan, Loay; Saleh, Adel; Abdel-Nasser, Mohamed; Omer, Osama A; Puig, Domenec
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
    Palabras clave: 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
    Resumen: 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.
    Áreas temáticas: Engineering, electrical & electronic Electrical and electronic engineering Computer science, artificial intelligence
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat
    Identificador del autor: 0000-0002-1074-2441 0000-0002-0562-4205
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.mdpi.com/2079-9292/10/8/954
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Trait Signal. 38 (3): 653-661
    Referencia 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 del artículo: 10.18280/ts.380312
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2021
    Tipo de publicación: Journal Publications
  • Palabras clave:

    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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