MRI augmentation via elastic registration for brain lesions segmentation

Egor Krivov, Maxim Pisov, Mikhail Belyaev

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

    7 Citations (Scopus)

    Abstract

    Datasets for medical image segmentation usually contain a very limited number of training examples. However, deep learning methods prove to be very competitive for such data analysis problems. Surprisingly, quite limited data augmentation is used during training. We presume that it’s due to historical reasons: standardization and normalization of medical images dominate over methods for increasing the size of a training set by artificial transformation of images. We assume that it is partly caused by the absence of methods which preserve properties of adequately preprocessed medical images. In this paper, we propose a new method for brain MRI augmentation, which allows us to map a lesion from an original image to a healthy brain. We compare the performance of U-Net and DeepMedic, two popular deep learning architectures, using the proposed method, a set of classical image augmentation methods, and a combination of both approaches. Our results suggest that at least one of the individual strategies, as well as their combination, provide an increase in accuracy of brain lesions segmentation if the training sample is relatively small.

    Original languageEnglish
    Title of host publicationBrainlesion
    Subtitle of host publicationGlioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 3rd International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Revised Selected Papers
    EditorsBjoern Menze, Alessandro Crimi, Hugo Kuijf, Mauricio Reyes, Spyridon Bakas
    PublisherSpringer Verlag
    Pages369-380
    Number of pages12
    ISBN (Print)9783319752372
    DOIs
    Publication statusPublished - 2018
    Event3rd International Workshop on Brainlesion, BrainLes 2017 Held in Conjunction with Medical Image Computing for Computer Assisted Intervention , MICCAI 2017 - Quebec City, Canada
    Duration: 14 Sep 201714 Sep 2017

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10670 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference3rd International Workshop on Brainlesion, BrainLes 2017 Held in Conjunction with Medical Image Computing for Computer Assisted Intervention , MICCAI 2017
    Country/TerritoryCanada
    CityQuebec City
    Period14/09/1714/09/17

    Keywords

    • BraTS
    • CNN
    • Image augmentation
    • MRI
    • Segmentation

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