Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application

Boris Shirokikh, Alexandra Dalechina, Alexey Shevtsov, Egor Krivov, Valery Kostjuchenko, Amayak Durgaryan, Mikhail Galkin, Andrey Golanov, Mikhail Belyaev

    Research output: Contribution to journalArticlepeer-review


    We systematically evaluate a Deep Learning model in a 3D medical image segmentation task. With our model, we address the flaws of manual segmentation: high inter-rater contouring variability and time consumption of the contouring process. The main extension over the existing evaluations is the careful and detailed analysis that could be further generalized on other medical image segmentation tasks. Firstly, we analyze the changes in the inter-rater detection agreement. We show that the model reduces the number of detection disagreements by 48% (p < 0.05). Secondly, we show that the model improves the inter-rater contouring agreement from 0.845 to 0.871 surface Dice Score (p < 0.05). Thirdly, we show that the model accelerates the delineation process between 1.6 and 2.0 times (p < 0.05). Finally, we design the setup of the clinical experiment to either exclude or estimate the evaluation biases; thus, preserving the significance of the results. Besides the clinical evaluation, we also share intuitions and practical ideas for building an efficient DL-based model for 3D medical image segmentation.
    Original languageEnglish
    JournalIEEE Journal of Biomedical and Health Informatics
    Publication statusPublished - 1 Jan 2022


    • Automatic delineation
    • Biomedical imaging
    • brain metastases
    • clinical evaluation
    • Convolutional neural networks
    • deep learning
    • Image segmentation
    • Lesions
    • radiosurgery
    • Solid modeling
    • Task analysis
    • Training


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