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    Transfer learning for data-efficient abdominal muscle segmentation with convolutional neural networks

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    Medical Physics - 2022 - McSweeney ...
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    Authors
    McSweeney, D. M.
    Henderson, E. G.
    Van Herk, Marcel
    Weaver, Jamie M
    Bromiley, P. A.
    Green, Andrew
    McWilliam, Alan
    Affiliation
    Division of Cancer Sciences, University of Manchester, Manchester, UK. Radiotherapy Related Research, The Christie Foundation Trust, Manchester, UK. Department of Medical Oncology, The Christie Hospital NHS Foundation Trust, Manchester, UK. Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK.
    Issue Date
    2022
    
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    Abstract
    Background Skeletal muscle segmentation is an important procedure for assessing sarcopenia, an emerging imaging biomarker of patient frailty. Data annotation remains the bottleneck for training deep learning auto-segmentation models. Purpose There is a need to define methodologies for applying models to different domains (e.g., anatomical regions or imaging modalities) without dramatically increasing data annotation. Methods To address this problem, we empirically evaluate the generalizability of various source tasks for transfer learning: natural image classification, natural image segmentation, unsupervised image reconstruction, and self-supervised jigsaw solving. Axial CT slices at L3 were extracted from PET-CT scans for 204 oesophago-gastric cancer patients and the skeletal muscle manually delineated by an expert. Features were transferred and segmentation models trained on subsets ( 𝑛=5,10,25,50,75,100,125 ) of the manually annotated training set. Four-fold cross-validation was performed to evaluate model generalizability. Human-level performance was established by performing an inter-observer study consisting of ten trained radiographers. Results We find that accurate segmentation models can be trained on a fraction of the data required by current approaches. The Dice similarity coefficient and root mean square distance-to-agreement were calculated for each prediction and used to assess model performance. Models pre-trained on a segmentation task and fine-tuned on 10 images produce delineations that are comparable to those from trained observers and extract reliable measures of muscle health. Conclusions Appropriate transfer learning can generate convolutional neural networks for abdominal muscle segmentation that achieve human-level performance while decreasing the required data by an order of magnitude, compared to previous methods ( 𝑛=160→10 ). This work enables the development of future models for assessing skeletal muscle at other anatomical sites where large annotated data sets are scarce and clinical needs are yet to be addressed.
    Citation
    McSweeney DM, Henderson EG, van Herk M, Weaver J, Bromiley PA, Green A, et al. Transfer learning for data‐efficient abdominal muscle segmentation with convolutional neural networks [Internet]. Medical Physics. Wiley; 2022.
    Journal
    Med Phys
    URI
    http://hdl.handle.net/10541/625126
    DOI
    10.1002/mp.15533
    PubMed ID
    35170063
    Additional Links
    https://dx.doi.org/10.1002/mp.15533
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1002/mp.15533
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