• Login
    View Item 
    •   UMB Digital Archive
    • UMB Open Access Articles
    • UMB Open Access Articles
    • View Item
    •   UMB Digital Archive
    • UMB Open Access Articles
    • UMB Open Access Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UMB Digital ArchiveCommunitiesPublication DateAuthorsTitlesSubjectsThis CollectionPublication DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    Display statistics

    Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Publisher version
    View Source
    Access full-text PDFOpen Access
    View Source
    Check access options
    Check access options
    Author
    Thakur, S.
    Doshi, J.
    Alexander, G.S.
    Date
    2020
    Journal
    NeuroImage
    Publisher
    Academic Press Inc.
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.1016/j.neuroimage.2020.117081
    Abstract
    Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ‘‘modality-agnostic training’’ technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors. Copyright 2020 The Author(s).
    Sponsors
    Research reported in this publication was partly supported by the National Institutes of Health (NIH) under award numbers NINDS:R01NS042645 , NCI:U24CA189523 , NCI:U01CA242871 .
    Keyword
    Brain Extraction
    Brain tumor
    Deep learning
    Evaluation
    Glioblastoma
    Glioma
    Skull-stripping
    TCIA
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087366850&doi=10.1016%2fj.neuroimage.2020.117081&partnerID=40&md5=6d129b0eca145e77a4c6cad9d5f4f504; http://hdl.handle.net/10713/13296
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.neuroimage.2020.117081
    Scopus Count
    Collections
    UMB Open Access Articles

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Policies | Contact Us | UMB Health Sciences & Human Services Library
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.