Incorporating task-specific structural knowledge into CNNs for brain midline shift detection

Maxim Pisov, Mikhail Goncharov, Nadezhda Kurochkina, Sergey Morozov, Victor Gombolevsky, Valeria Chernina, Anton Vladzymyrskyy, Ksenia Zamyatina, Anna Cheskova, Igor Pronin, Michael Shifrin, Mikhail Belyaev

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    9 Citations (Scopus)

    Abstract

    Midline shift (MLS) is a well-established factor used for outcome prediction in traumatic brain injury, stroke and brain tumors. The importance of automatic estimation of MLS was recently highlighted by ACR Data Science Institute. In this paper we introduce a novel deep learning based approach for the problem of MLS detection, which exploits task-specific structural knowledge. We evaluate our method on a large dataset containing heterogeneous images with significant MLS and show that its mean error approaches the inter-expert variability. Finally, we show the robustness of our approach by validating it on an external dataset, acquired during routine clinical practice.

    Original languageEnglish
    Title of host publicationInterpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support - 2nd International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Proceedings
    EditorsKenji Suzuki, Mauricio Reyes, Tanveer Syeda-Mahmood, Yaniv Gur, Ben Glocker, Ender Konukoglu, Roland Wiest, Hayit Greenspan, Anant Madabhushi
    PublisherSpringer
    Pages30-38
    Number of pages9
    ISBN (Print)9783030338497
    DOIs
    Publication statusPublished - 2019
    Event2nd International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
    Duration: 17 Oct 201917 Oct 2019

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11797 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference2nd International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019
    Country/TerritoryChina
    CityShenzhen
    Period17/10/1917/10/19

    Keywords

    • Confidence
    • Interpretability
    • Midline shift
    • Neural networks

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