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Promising Deep Semantic Nuclei Segmentation Models for Multi-Institutional Histopathology Images of Different Organs

  • Identification data

    Identifier: imarina:9218757
    Authors:
    Hassan, LoaySaleh, AdelAbdel-Nasser, MohamedOmer, Osama APuig, Domenec
    Abstract:
    Nuclei segmentation in whole-slide imaging (WSI) plays a crucial role in the field of computational pathology. It is a fundamental task for different applications, such as cancer cell type classification, cancer grading, and cancer subtype classification. However, existing nuclei segmentation methods face many challenges, such as color variation in histopathological images, the overlapping and clumped nuclei, and the ambiguous boundary between different cell nuclei, that limit their performance. In this paper, we present promising deep semantic nuclei segmentation models for multi-institutional WSI images (i.e., collected from different scanners) of different organs. Specifically, we study the performance of pertinent deep learning-based models with nuclei segmentation in WSI images of different stains and various organs. We also propose a feasible deep learning nuclei segmentation model formed by combining robust deep learning architectures. A comprehensive comparative study with existing software and related methods in terms of different evaluation metrics and the number of parameters of each model, emphasizes the efficacy of the proposed nuclei segmentation models.
  • Others:

    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: Whole slide imaging Nuclei segmentation Digital pathology Deep learning
    Abstract: Nuclei segmentation in whole-slide imaging (WSI) plays a crucial role in the field of computational pathology. It is a fundamental task for different applications, such as cancer cell type classification, cancer grading, and cancer subtype classification. However, existing nuclei segmentation methods face many challenges, such as color variation in histopathological images, the overlapping and clumped nuclei, and the ambiguous boundary between different cell nuclei, that limit their performance. In this paper, we present promising deep semantic nuclei segmentation models for multi-institutional WSI images (i.e., collected from different scanners) of different organs. Specifically, we study the performance of pertinent deep learning-based models with nuclei segmentation in WSI images of different stains and various organs. We also propose a feasible deep learning nuclei segmentation model formed by combining robust deep learning architectures. A comprehensive comparative study with existing software and related methods in terms of different evaluation metrics and the number of parameters of each model, emphasizes the efficacy of the proposed nuclei segmentation models.
    Thematic Areas: Statistics and probability Signal processing Linguística e literatura Interdisciplinar Engenharias iv Educação Computer vision and pattern recognition Computer science, interdisciplinary applications Computer science, artificial intelligence Computer science applications Computer networks and communications Ciência da computação 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.ijimai.org/journal/bibcite/reference/2827
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: International Journal Of Interactive Multimedia And Artificial Intelligence. 6 (6): 35-45
    APA: Hassan, Loay; Saleh, Adel; Abdel-Nasser, Mohamed; Omer, Osama A; Puig, Domenec (2021). Promising Deep Semantic Nuclei Segmentation Models for Multi-Institutional Histopathology Images of Different Organs. International Journal Of Interactive Multimedia And Artificial Intelligence, 6(6), 35-45. DOI: 10.9781/ijimai.2020.10.004
    Article's DOI: 10.9781/ijimai.2020.10.004
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2021
    Publication Type: Journal Publications
  • Keywords:

    Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Computer Science, Artificial Intelligence,Computer Science, Interdisciplinary Applications,Computer Vision and Pattern Recognition,Signal Processing,Statistics and Probability
    Whole slide imaging
    Nuclei segmentation
    Digital pathology
    Deep learning
    Statistics and probability
    Signal processing
    Linguística e literatura
    Interdisciplinar
    Engenharias iv
    Educação
    Computer vision and pattern recognition
    Computer science, interdisciplinary applications
    Computer science, artificial intelligence
    Computer science applications
    Computer networks and communications
    Ciência da computação
    Artificial intelligence
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