Brain structure segmentation in the presence of multiple sclerosis lesions.

Clicks: 318
ID: 74948
2019
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Ranked #15 of 27 articles by views in neuroimage clinical

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Abstract
Intensity-based multi-atlas segmentation strategies have shown to be particularly successful in segmenting brain images of healthy subjects. However, in the same way as most of the methods in the state of the art, their performance tends to be affected by the presence of MRI visible lesions, such as those found in multiple sclerosis (MS) patients. Here, we present an approach to minimize the effect of the abnormal lesion intensities on multi-atlas segmentation. We propose a new voxel/patch correspondence model for intensity-based multi-atlas label fusion strategies that leads to more accurate similarity measures, having a key role in the final brain segmentation. We present the theory of this model and integrate it into two well-known fusion strategies: Non-local Spatial STAPLE (NLSS) and Joint Label Fusion (JLF). The experiments performed show that our proposal improves the segmentation performance of the lesion areas. The results indicate a mean Dice Similarity Coefficient (DSC) improvement of 1.96% for NLSS (3.29% inside and 0.79% around the lesion masks) and, an improvement of 2.06% for JLF (2.31% inside and 1.42% around lesions). Furthermore, we show that, with the proposed strategy, the well-established preprocessing step of lesion filling can be disregarded, obtaining similar or even more accurate segmentation results.
Reference Key
gonzlezvill2019brainneuroimage Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors González-Villà, Sandra;Oliver, Arnau;Huo, Yuankai;Lladó, Xavier;Landman, Bennett A;
Journal neuroimage clinical
Year 2019
DOI
S2213-1582(19)30059-2
URL
Keywords
MRI Segmentation brain structures label fusion multi-atlas multiple sclerosis lesions parcellation

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