Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation.
brain tumor segmentation
convolutional neural networks
deep learning
deformable registration
multi-task networks
Journal
Frontiers in computational neuroscience
ISSN: 1662-5188
Titre abrégé: Front Comput Neurosci
Pays: Switzerland
ID NLM: 101477956
Informations de publication
Date de publication:
2020
2020
Historique:
received:
03
07
2019
accepted:
11
02
2020
entrez:
9
4
2020
pubmed:
9
4
2020
medline:
9
4
2020
Statut:
epublish
Résumé
Image registration and segmentation are the two most studied problems in medical image analysis. Deep learning algorithms have recently gained a lot of attention due to their success and state-of-the-art results in variety of problems and communities. In this paper, we propose a novel, efficient, and multi-task algorithm that addresses the problems of image registration and brain tumor segmentation jointly. Our method exploits the dependencies between these tasks through a natural coupling of their interdependencies during inference. In particular, the similarity constraints are relaxed within the tumor regions using an efficient and relatively simple formulation. We evaluated the performance of our formulation both quantitatively and qualitatively for registration and segmentation problems on two publicly available datasets (BraTS 2018 and OASIS 3), reporting competitive results with other recent state-of-the-art methods. Moreover, our proposed framework reports significant amelioration (
Identifiants
pubmed: 32265680
doi: 10.3389/fncom.2020.00017
pmc: PMC7100603
doi:
Types de publication
Journal Article
Langues
eng
Pagination
17Subventions
Organisme : NIA NIH HHS
ID : P01 AG003991
Pays : United States
Organisme : NIA NIH HHS
ID : P01 AG026276
Pays : United States
Informations de copyright
Copyright © 2020 Estienne, Lerousseau, Vakalopoulou, Alvarez Andres, Battistella, Carré, Chandra, Christodoulidis, Sahasrabudhe, Sun, Robert, Talbot, Paragios and Deutsch.
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