Multiple sclerosis lesion enhancement and white matter region estimation using hyperintensities in FLAIR images
This work addresses the challenge of improving lesion visibility for diagnosis and automated segmentation in multiple sclerosis, a disease affecting over 2 million people, though it appears incremental as it builds on existing imaging techniques.
The paper tackled the problem of accurately segmenting multiple sclerosis lesions in MRI FLAIR images, where lesions overlap with white and gray matter, by proposing a lesion enhancement technique that significantly increased the intensity distinction, with lesions becoming 444% and 264% brighter than white and gray matter, respectively, in enhanced images compared to 25% and 19% in original FLAIR images.
Multiple sclerosis (MS) is a demyelinating disease that affects more than 2 million people worldwide. The most used imaging technique to help in its diagnosis and follow-up is magnetic resonance imaging (MRI). Fluid Attenuated Inversion Recovery (FLAIR) images are usually acquired in the context of MS because lesions often appear hyperintense in this particular image weight, making it easier for physicians to identify them. Though lesions have a bright intensity profile, it may overlap with white matter (WM) and gray matter (GM) tissues, posing difficulties to be accurately segmented. In this sense, we propose a lesion enhancement technique to dim down WM and GM regions and highlight hyperintensities, making them much more distinguishable than other tissues. We applied our technique to the ISBI 2015 MS Lesion Segmentation Challenge and took the average gray level intensity of MS lesions, WM and GM on FLAIR and enhanced images. The lesion intensity profile in FLAIR was on average 25% and 19% brighter than white matter and gray matter, respectively; comparatively, the same profile in our enhanced images was on average 444% and 264% brighter. Such results mean a significant improvement on the intensity distinction among these three clusters, which may come as aid both for experts and automated techniques. Moreover, a byproduct of our proposal is that the enhancement can be used to automatically estimate a mask encompassing WM and MS lesions, which may be useful for brain tissue volume assessment and improve MS lesion segmentation accuracy in future works.