Model Selection Propagation for Application on Longitudinal Ms Lesion Segmentation
نویسندگان
چکیده
Despite possible structural changes related to atrophy and edema, the structural anatomy of the brain should present time consistency for a given patient. Based on this assumption, we propose a lesion segmentation method that first derives a gaussian mixture model (GMM) separating healthy tissues from pathological and unexpected ones on a multi-time-point intra-subject groupwise image. This average patient-specific GMM is then propagated back to each time point where it serves as an initialization to the final time point specific GMM from which the final lesion segmentations are obtained.
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