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3 April 2023 Volumetric measurements of brain in multiple sclerosis: comparison of segmentation methods in simulated MRI data
Phelipi N. Schuck, Adriel S. Araújo, Dimitri B. A. Mantovani, Nícollas G. Cavedini, Ana Maria Marques da Silva
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Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 1246436 (2023) https://doi.org/10.1117/12.2654417
Event: SPIE Medical Imaging, 2023, San Diego, California, United States
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Abstract
High-resolution structural magnetic resonance imaging (MRI) allows neurological investigation, especially when brain volumes must be carefully delineated to monitor neurodegeneration, such as in multiple sclerosis (MS). This study compares different segmentation techniques applied to brain MRI to measure the white matter (WM) and grey matter (GM) in healthy and MS brains. We propose to evaluate the reliability and how each segmentation method could potentially affect clinical trials in MS. Four segmentation software were evaluated: Statistical Parametric Mapping (SPM), Lesion Segmentation Tool (LST), Freesurfer, and Siena/X. We simulated healthy and MS brain MRI and compared the segmentation volumes with the ground truth. Our results showed that LST provides overall good segmentation with low variability. When SienaX spatially normalizes the images, the WM and GM volumes are overestimated. On the other hand, Freesurfer underestimates volumes. We conclude that the use of different segmentation software produces variability in GM and WM volumes, especially in challenging situations, such as small lesions and in the presence of noise. The best method was the automatic region growth algorithm implemented using the LST, which uses T1-weighted and T2-FLAIR MRI.
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Phelipi N. Schuck, Adriel S. Araújo, Dimitri B. A. Mantovani, Nícollas G. Cavedini, and Ana Maria Marques da Silva "Volumetric measurements of brain in multiple sclerosis: comparison of segmentation methods in simulated MRI data", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 1246436 (3 April 2023); https://doi.org/10.1117/12.2654417
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KEYWORDS
Image segmentation
Brain
Magnetic resonance imaging
Multiple sclerosis
Neuroimaging
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