A Generative Model for Automatic Detection of Resolving Multiple Sclerosis Lesions
The appearance of new Multiple Sclerosis (MS) lesions on MRI is usually followed by subsequent partial resolution, where portions of the newly formed lesion return to isointensity. This resolution is thought to be due mostly to reabsorption of edema, but may also reflect other reparatory processes such as remyelination. Automatic identification of resolving portions of new lesions can provide a marker of repair, allow for automated analysis of MS lesion dynamics, and, when coupled with a method for detection of new MS lesions, provide a tool for precisely measuring lesion change in serial MRI. We present a method for automatic detection of resolving MS lesion voxels in serial MRI using a Bayesian framework that incorporates models for MRI intensities, MRI intensity differences across scans, lesion size, relative position of voxels within a lesion, and time since lesion onset. We couple our method with an existing method for automatic detection of new MS lesions to provide an automated framework for measuring lesion change across serial scans of the same subject. We validate our framework by comparing to lesion volume change measurements derived from expert semi-manual lesion segmentations on clinical trial data consisting of 292 scans from 73 (54 treated, 19 untreated) subjects. Our automated framework shows a) a large improvement in segmentation consistency over time and b) an increased effect size as calculated from measured change in lesion volume for treated and untreated subjects.
KeywordsMultiple Sclerosis Lesion Volume Multiple Sclerosis Lesion Resolution Status Temporal Segmentation
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