Available on ORBilu since
02 December 2022
Article (Scientific journals)
DBSegment: Fast and robust segmentation of deep brain structures considering domain generalisation
Baniasadi, Mehri; Petersen, Mikkel V.; Goncalves, Jorge et al.
2022In Human Brain Mapping
Peer Reviewed verified by ORBi


Full Text
Human Brain Mapping - 2022 - Baniasadi - DBSegment Fast and robust segmentation of deep brain structures considering.pdf
Publisher postprint (3 MB)

All documents in ORBilu are protected by a user license.

Send to


Keywords :
Segmentation; Deep Learning; MRI; Confounder
Abstract :
[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 :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
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
External co-authors :
Language :
Title :
DBSegment: Fast and robust segmentation of deep brain structures considering domain generalisation
Publication date :
17 October 2022
Journal title :
Human Brain Mapping
Publisher :
John Wiley & Sons, Hoboken, United States - New York
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Computational Sciences
FnR Project :
FNR12548237 > Mehri Baniasadi > TreCoDBS > Personalised Tremor Control By Advanced Clinical Deep Brain Stimulation. > 01/11/2018 > 31/10/2022 > 2018
Funders :
FNR - Fonds National de la Recherche
Emmy Noether stipend
jascha fonden
DFG - Deutsche Forschungsgemeinschaft
Berlin institute ion health
111 Project on computational intelligence and intelligent control
National institute of health


Number of views
51 (5 by Unilu)
Number of downloads
58 (2 by Unilu)

Scopus citations®
Scopus citations®
without self-citations
WoS citations


Similar publications

Contact ORBilu