Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels

Théo Estienne, Maria Vakalopoulou, Enzo Battistella, Alexandre Carré, Théophraste Henry, Marvin Lerousseau, Charlotte Robert, Nikos Paragios, Eric Deutsch

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

    5 Citations (Scopus)

    Abstract

    Image registration is one of the most challenging problems in medical image analysis. In the recent years, deep learning based approaches became quite popular, providing fast and performing registration strategies. In this short paper, we summarise our work presented on Learn2Reg challenge 2020. The main contributions of our work rely on (i) a symmetric formulation, predicting the transformations from source to target and from target to source simultaneously, enforcing the trained representations to be similar and (ii) integration of variety of publicly available datasets used both for pretraining and for augmenting segmentation labels. Our method reports a mean dice of 0.64 for task 3 and 0.85 for task 4 on the test sets, taking third place on the challenge. Our code and models are publicly available at https://github.com/TheoEst/abdominal_registration and https://github.com/TheoEst/hippocampus_registration.

    Original languageEnglish
    Title of host publicationSegmentation, Classification, and Registration of Multi-modality Medical Imaging Data - MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Proceedings
    EditorsNadya Shusharina, Mattias P. Heinrich, Ruobing Huang
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages87-93
    Number of pages7
    ISBN (Print)9783030718268
    DOIs
    Publication statusPublished - 1 Jan 2021
    EventAnatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and Thyroid Nodule Segmentation and Classification in Ultrasound Images Challenge, TN-SCUI 2020 held in conjunction with 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
    Duration: 4 Oct 20208 Oct 2020

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume12587 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    ConferenceAnatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and Thyroid Nodule Segmentation and Classification in Ultrasound Images Challenge, TN-SCUI 2020 held in conjunction with 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
    Country/TerritoryPeru
    CityLima
    Period4/10/208/10/20

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