Buchumschlag

A Comparison Between Unimodal and Multimodal Segmentation Models for Deep Brain Structures from T1- and T2-Weighted MRI

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Veröffentlicht in:Machine Learning and Knowledge Extraction vol. 7, no. 3 (2025), p. 84-108
1. Verfasser: Altini Nicola
Weitere Verfasser: Lasaracina Erica, Galeone Francesca, Prunella Michela, Suglia Vladimiro, Carnimeo Leonarda, Triggiani Vito, Ranieri Daniele, Brunetti Gioacchino, Bevilacqua Vitoantonio
Veröffentlicht:
MDPI AG
Schlagworte:
University of North Carolina
Datasets
Neuroimaging
Alzheimer's disease
Deep learning
Image segmentation
Brain research
Magnetic resonance imaging
Brain
Data collection
Automation
Medical imaging
Longitudinal studies
Movement disorders
Parkinson's disease
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Abstract:Accurate segmentation of deep brain structures is critical for preoperative planning in such neurosurgical procedures as Deep Brain Stimulation (DBS). Previous research has showcased successful pipelines for segmentation from T1-weighted (T1w) Magnetic Resonance Imaging (MRI) data. Nevertheless, the role of T2-weighted (T2w) MRI data has been underexploited so far. This study proposes and evaluates a fully automated deep learning pipeline based on nnU-Net for the segmentation of eight clinically relevant deep brain structures. A heterogeneous dataset has been prepared by gathering 325 paired T1w and T2w MRI scans from eight publicly available sources, which have been annotated by means of an atlas-based registration approach. Three 3D nnU-Net models—unimodal T1w, unimodal T2w, and multimodal (encompassing both T1w and T2w)—have been trained and compared by using 5-fold cross-validation and a separate test set. The outcomes prove that the multimodal model consistently outperforms the T2w unimodal model and achieves comparable performance with the T1w unimodal model. On our dataset, all proposed models significantly exceed the performance of the state-of-the-art DBSegment tool. These findings underscore the value of multimodal MRI in enhancing deep brain segmentation and offer a robust framework for accurate delineation of subcortical targets in both research and clinical settings.
ISSN:2504-4990
DOI:10.3390/make7030084
Quelle:Advanced Technologies & Aerospace Database