A domain adaptation benchmark for T1-weighted brain magnetic resonance image segmentation.

Autor:	Saat P; Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada., Nogovitsyn N; Centre for Depression and Suicide Studies, St. Michael's Hospital, Toronto, ON, Canada.; Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada., Hassan MY; Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada.; Electrical Engineering, Indian Institute of Technology, Gandhinagar, Gujarat, India., Ganaie MA; Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada.; Chemical Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India., Souza R; Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada.; Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada., Hemmati H; Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada.; Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada.
Jazyk:	angličtina
Zdroj:	Frontiers in neuroinformatics [Front Neuroinform] 2022 Sep 23; Vol. 16, pp. 919779. Date of Electronic Publication: 2022 Sep 23 (Print Publication: 2022).
DOI:	10.3389/fninf.2022.919779
Abstrakt:	Accurate brain segmentation is critical for magnetic resonance imaging (MRI) analysis pipelines. Machine-learning-based brain MR image segmentation methods are among the state-of-the-art techniques for this task. Nevertheless, the segmentations produced by machine learning models often degrade in the presence of expected domain shifts between the test and train sets data distributions. These domain shifts are expected due to several factors, such as scanner hardware and software differences, technology updates, and differences in MRI acquisition parameters. Domain adaptation (DA) methods can make machine learning models more resilient to these domain shifts. This paper proposes a benchmark for investigating DA techniques for brain MR image segmentation using data collected across sites with scanners from different vendors (Philips, Siemens, and General Electric). Our work provides labeled data, publicly available source code for a set of baseline and DA models, and a benchmark for assessing different brain MR image segmentation techniques. We applied the proposed benchmark to evaluate two segmentation tasks: skull-stripping; and white-matter, gray-matter, and cerebrospinal fluid segmentation, but the benchmark can be extended to other brain structures. Our main findings during the development of this benchmark are that there is not a single DA technique that consistently outperforms others, and hyperparameter tuning and computational times for these methods still pose a challenge before broader adoption of these methods in the clinical practice. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Saat, Nogovitsyn, Hassan, Ganaie, Souza and Hemmati.)
Databáze:	MEDLINE
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