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2 April 2024 Multimodal segmentation for paramagnetic rim lesion detection in multiple sclerosis
Maxence Wynen, Pedro M. Gordaliza, Anna Stölting, Pietro Maggi, Meritxell Bach Cuadra, Benoit Macq
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Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 1293112 (2024) https://doi.org/10.1117/12.3005951
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
Abstract
Magnetic Resonance Imaging (MRI) plays a pivotal role in diagnosing and predicting the course of Multiple Sclerosis (MS). A distinctive biomarker, Paramagnetic Rim Lesions (PRL), offers promise but poses challenges in manual assessment. To address this, we introduce a direct PRL segmentation approach and extensively evaluate various methods, with a focus on preprocessing and input modalities. Our study emphasizes instance segmentation metrics tailored for sparse lesions. Single-modal inputs show limitations, except for FLAIR and Magnitude, exhibiting potential in PRL detection. Integrating Phase and/or MPRAGE with FLAIR enhances the detection capacity. Notably, applying white matter masks yields mixed results, while lesion masks improve overall performance. Despite the complexities of PRL segmentation, our optimal model, FLAIR+Phase, attains a F1 score of 0.443, a Dice score coefficient per True Positive of 0.68 and a deceiving Dice score of 0.191 on the test set. This highlights the intricate nature of the PRL segmentation task. Our work pioneers an automated approach to PRL analysis, offering valuable insights and paving the way for future advancements in this critical field.
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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Maxence Wynen, Pedro M. Gordaliza, Anna Stölting, Pietro Maggi, Meritxell Bach Cuadra, and Benoit Macq "Multimodal segmentation for paramagnetic rim lesion detection in multiple sclerosis", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 1293112 (2 April 2024); https://doi.org/10.1117/12.3005951
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KEYWORDS
Image segmentation
Multiple sclerosis
Magnetic resonance imaging
Object detection
Machine learning
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