[en] Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage.
Disciplines :
Ingénierie, informatique & technologie: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
BANIASADI, Mehri ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Control
Petersen, Mikkel V.; University of Aarhus
GONCALVES, Jorge ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Control
Horn, Andreas; Harvard University > Harvard Medical School ; Charite Medical University of Berlin
Vlasov, Vanja
HERTEL, Frank ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC)
HUSCH, Andreas ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Interventional Neuroscience
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
DBSegment: Fast and robust segmentation of deep brain structures considering domain generalisation
FNR12548237 - Personalised Tremor Control By Advanced Clinical Deep Brain Stimulation., 2018 (01/11/2018-31/10/2022) - Mehri Baniasadi
Organisme subsidiant :
FNR - Fonds National de la Recherche Lundbeckfonden Emmy Noether stipend jascha fonden DFG - Deutsche Forschungsgemeinschaft Berlin institute ion health 111 Project on computational intelligence and intelligent control National institute of health
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