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    Optimising a 3D convolutional neural network for head and neck computed tomography segmentation with limited training data

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    Authors
    Henderson, E. G. A.
    Vasquez Osorio, Eliana
    van Herk, Marcel
    Green, Andrew
    Affiliation
    The University of Manchester, Oxford Rd, Manchester M13 9PL, UK
    Issue Date
    2022
    
    Metadata
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    Abstract
    Background and purpose: Convolutional neural networks (CNNs) are increasingly used to automate segmentation for radiotherapy planning, where accurate segmentation of organs-at-risk (OARs) is crucial. Training CNNs often requires large amounts of data. However, large, high quality datasets are scarce. The aim of this study was to develop a CNN capable of accurate head and neck (HN) 3D auto-segmentation of planning CT scans using a small training dataset (34 CTs). Materials and method: Elements of our custom CNN architecture were varied to optimise segmentation performance. We tested and evaluated the impact of: using multiple contrast channels for the CT scan input at specific soft tissue and bony anatomy windows, resize vs. transpose convolutions, and loss functions based on overlap metrics and cross-entropy in different combinations. Model segmentation performance was compared with the inter-observer deviation of two doctors' gold standard segmentations using the 95th percentile Hausdorff distance and mean distance-to-agreement (mDTA). The best performing configuration was further validated on a popular public dataset to compare with state-of-the-art (SOTA) auto-segmentation methods. Results: Our best performing CNN configuration was competitive with current SOTA methods when evaluated on the public dataset with mDTA of (0.81±0.31) mm for the brainstem, 0.20±0.08) mm for the mandible, 0.77±0.14) mm for the left parotid and (0.81±0.28)m for the right parotid. Conclusions: Through careful tuning and customisation we trained a 3D CNN with a small dataset to produce segmentations of HN OARs with an accuracy that is comparable with inter-clinician deviations. Our proposed model performed competitively with current SOTA methods.
    Citation
    Henderson EGA, Vasquez Osorio EM, van Herk M, Green AF. Optimising a 3D convolutional neural network for head and neck computed tomography segmentation with limited training data. Vol. 22, Physics and Imaging in Radiation Oncology. Elsevier BV; 2022. p. 44–50.
    Journal
    Physics and Imaging in Radiation Oncology
    URI
    http://hdl.handle.net/10541/625298
    DOI
    10.1016/j.phro.2022.04.003
    PubMed ID
    35514528
    Additional Links
    https://dx.doi.org/10.1016/j.phro.2022.04.003
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.phro.2022.04.003
    Scopus Count
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