Comparison of atlas-based and neural-network-based semantic segmentation for DENSE MRI images

• URL: http://arxiv.org/abs/2109.14116v1
• Date: Wed, 29 Sep 2021 00:42:43 GMT
• Title: Comparison of atlas-based and neural-network-based semantic segmentation for DENSE MRI images
• Authors: Elle Buser, Emma Hart, Ben Huenemann
• Abstract summary: Two segmentation methods, one atlas-based and one neural-network-based, were compared. The segmentation is a pre-requisite for estimating the average displacements in these regions.
• Score: 0.8701566919381223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Two segmentation methods, one atlas-based and one neural-network-based, were compared to see how well they can each automatically segment the brain stem and cerebellum in Displacement Encoding with Stimulated Echoes Magnetic Resonance Imaging (DENSE-MRI) data. The segmentation is a pre-requisite for estimating the average displacements in these regions, which have recently been proposed as biomarkers in the diagnosis of Chiari Malformation type I (CMI). In numerical experiments, the segmentations of both methods were similar to manual segmentations provided by trained experts. It was found that, overall, the neural-network-based method alone produced more accurate segmentations than the atlas-based method did alone, but that a combination of the two methods -- in which the atlas-based method is used for the segmentation of the brain stem and the neural-network is used for the segmentation of the cerebellum -- may be the most successful.

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