UHF-MRI; brain segmentation; contrast and resolution agnosticity; deep learning; domain randomization; neuroimaging; Neuroscience (miscellaneous); Medicine (miscellaneous); Radiology, Nuclear Medicine and Imaging; Neurology (clinical)
Abstract :
[en] Recently, ultra-high-field MRI (UHF-MRI) has become more available and one of the best tools to study the brain for neuroscientists. One common step in quantitative neuroimaging is to segment the brain into several regions, which has been done using software packages such as FreeSurfer, FastSurferVINN, or SynthSeg. However, the differences between UHF-MRI and 1.5T or 3T images are such that the automatic segmentation techniques optimized at these field strengths usually produce unsatisfactory segmentation results for UHF images. Thus, it has been particularly challenging to perform region-based quantitative analyses as typically done with 1.5-3T data, considerably limiting the potential of UHF-MRI until now. Ultimately, this underscores the crucial need for developing new automatic segmentation techniques designed to handle UHF images. Hence, we propose a novel Deep Learning (DL)-based segmentation technique called GOUHFI: Generalized and Optimized segmentation tool for ultra-high-field images, designed to segment UHF images of various contrasts and resolutions. For training, we used a total of 206 label maps from four datasets acquired at 3T, 7T, and 9.4T. In contrast to most DL strategies, we used a previously proposed domain randomization approach, where synthetic images generated from the 206 label maps were used for training a 3D U-Net. This approach enables the DL model to become contrast agnostic. GOUHFI was tested on seven different datasets and compared with existing techniques such as FastSurferVINN, SynthSeg, and CEREBRUM-7T. GOUHFI was able to segment the six contrasts and seven resolutions tested at 3T, 7T, and 9.4T. Average Dice-Sørensen Similarity Coefficient (DSC) scores of 0.90, 0.90, and 0.93 were computed against the ground truth segmentations at 3T, 7T, and 9.4T, respectively. These results demonstrated GOUHFI's superior performance to competing approaches at each resolution and contrast level tested. Moreover, GOUHFI demonstrated impressive resistance to the typical inhomogeneities observed at UHF-MRI, making it a new powerful segmentation tool allowing the usual quantitative analysis pipelines performed at lower fields to be applied also at UHF. Ultimately, GOUHFI is a promising new segmentation tool, being the first of its kind proposing a contrast- and resolution-agnostic alternative for UHF-MRI without requiring fine tuning or retraining, making it the forthcoming alternative for neuroscientists working with UHF-MRI or even lower field strengths.
Disciplines :
Neurology
Author, co-author :
Fortin, Marc-Antoine ; Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
Kristoffersen, Anne Louise; Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
Larsen, Michael Staff; Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
Lamalle, Laurent ; Université de Liège - ULiège > Département de physique
Stirnberg, Rüdiger; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
Goa, Pål Erik; Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway ; Department of Radiology and Nuclear Medicine, St. Olavs Hospital HF, Trondheim, Norway
Language :
English
Title :
GOUHFI: A novel contrast- and resolution-agnostic segmentation tool for ultra-high-field MRI.
F.R.S.-FNRS - Fonds de la Recherche Scientifique BMBF - Federal Ministry of Education and Research RCN - Research Council of Norway
Funding text :
This project and SCAIFIELD data acquisition were supported through the following funding organizations under the aegis of JPND: Belgium, The Fund for Scientific Research (F.R.S.-FNRS; funding code PINT-MULTI/BEJR.8006.20); Germany, Federal Ministry of Education and Research (BMBF; funding codes 01ED2109A/B); and Norway, The Research Council of Norway (RCN; funding code 322980).We would also like to thank all the publicly available datasets used in this work. First, data provided as part as the Human Connectome Project, WU-Minn Consortium (principal investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University were used in this publication. Finally, we would also like to thank individually all these following projects/initiatives for sharing their dataset and making this article possible: (1) the UltraCortex: Submillimeter Ultra-High Field 9.4 T1 Brain MR Image Collection and Manual Cortical Segmentations dataset (DOI: https://doi.org/10.18112/openneuro.ds005216.v1.1.0 ), (2) Autism Brain Imaging Data Exchange II (ABIDE-II) initiative (DOI: https://dx.doi.org/10.21227/y3v9-b041 ), (3) Open Science CBS Neuroimaging Repository ( https://www.nitrc.org/frs/?group_id=606 ), (4) CEREBRUM-7T: Fast and Fully-volumetric Brain Segmentation of 7 Tesla MR Volumes (DOI: https://doi.org/10.25493/RF12-09N ), and (5) Human Brain Atlas ( https://osf.io/ckh5t/ ).
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