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    Automated Gland Detection in Colorectal Histopathological Images

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    Publication date
    2022-01
    Author
    Al Zorgani, Maisun M.
    Mehmood, Irfan
    Ugail, Hassan
    Keyword
    Colon gland semantic segmentation
    Colorectal adenocarcinoma
    Deep learning
    Histopathological image analysis
    Peer-Reviewed
    Yes
    Open Access status
    closedAccess
    
    Metadata
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    Abstract
    Clinical morphological analysis of histopathological specimens is a successful manner for diagnosing benign and malignant diseases. Analysis of glandular architecture is a major challenge for colon histopathologists as a result of the difficulty of identifying morphological structures in glandular malignant tumours due to the distortion of glands boundaries, furthermore the variation in the appearance of staining specimens. For reliable analysis of colon specimens, several deep learning methods have exhibited encouraging performance in the glands automatic segmentation despite the challenges. In the histopathology field, the vast number of annotation images for training the deep learning algorithms is the major challenge. In this work, we propose a trainable Convolutional Neural Network (CNN) from end to end for detecting the glands automatically. More specifically, the Modified Res-U-Net is employed for segmenting the colorectal glands in Haematoxylin and Eosin (H&E) stained images for challenging Gland Segmentation (GlaS) dataset. The proposed Res-U-Net outperformed the prior methods that utilise U-Net architecture on the images of the GlaS dataset.
    URI
    http://hdl.handle.net/10454/18814
    Version
    No full-text in the repository
    Citation
    Al Zorgani MM, Mehmood I and Ugail H (2022) Automated Gland Detection in Colorectal Histopathological Images. In: Su R, Zhang YD and Liu H (Eds) Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). 25-26 Mar 2021. Birmingham, UK. Lecture Notes in Electrical Engineering. 784. Springer, Singapore. 153-162.
    Link to publisher’s version
    https://doi.org/10.1007/978-981-16-3880-0_17
    Type
    Conference paper
    Collections
    Engineering and Informatics Publications

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