Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-03-27

AUTHORS

Andrei Iantsen, Marta Ferreira, Francois Lucia, Vincent Jaouen, Caroline Reinhold, Pietro Bonaffini, Joanne Alfieri, Ramon Rovira, Ingrid Masson, Philippe Robin, Augustin Mervoyer, Caroline Rousseau, Frédéric Kridelka, Marjolein Decuypere, Pierre Lovinfosse, Olivier Pradier, Roland Hustinx, Ulrike Schick, Dimitris Visvikis, Mathieu Hatt

ABSTRACT

PurposeIn this work, we addressed fully automatic determination of tumor functional uptake from positron emission tomography (PET) images without relying on other image modalities or additional prior constraints, in the context of multicenter images with heterogeneous characteristics.MethodsIn cervical cancer, an additional challenge is the location of the tumor uptake near or even stuck to the bladder. PET datasets of 232 patients from five institutions were exploited. To avoid unreliable manual delineations, the ground truth was generated with a semi-automated approach: a volume containing the tumor and excluding the bladder was first manually determined, then a well-validated, semi-automated approach relying on the Fuzzy locally Adaptive Bayesian (FLAB) algorithm was applied to generate the ground truth. Our model built on the U-Net architecture incorporates residual blocks with concurrent spatial squeeze and excitation modules, as well as learnable non-linear downsampling and upsampling blocks. Experiments relied on cross-validation (four institutions for training and validation, and the fifth for testing).ResultsThe model achieved good Dice similarity coefficient (DSC) with little variability across institutions (0.80 ± 0.03), with higher recall (0.90 ± 0.05) than precision (0.75 ± 0.05) and improved results over the standard U-Net (DSC 0.77 ± 0.05, recall 0.87 ± 0.02, precision 0.74 ± 0.08). Both vastly outperformed a fixed threshold at 40% of SUVmax (DSC 0.33 ± 0.15, recall 0.52 ± 0.17, precision 0.30 ± 0.16). In all cases, the model could determine the tumor uptake without including the bladder. Neither shape priors nor anatomical information was required to achieve efficient training.ConclusionThe proposed method could facilitate the deployment of a fully automated radiomics pipeline in such a challenging multicenter context. More... »

PAGES

3444-3456

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00259-021-05244-z

    DOI

    http://dx.doi.org/10.1007/s00259-021-05244-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1136721543

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/33772335


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