Automated System for Multiple Sclerosis Lesion Segmentation in 3D Brain MRI

  • 劉 彥良

Student thesis: Master's Thesis


Multiple Sclerosis (MS) is a relatively common inflammatory disease involving the central nervous system MS lesions vary greatly in shape location intensity and area which challenge the automated segmentation methods Thus the lesion load and its delineation have established their importance for assessing disease progression This work built an automated system includes brain extraction registration atlas model creation bias correction and tissue segmentation with MS lesions and other tissues In this work we further extend Multi-channel MICO (MCMICO) algorithm and modify the energy formulation by introducing the atlas probability model into the MR images According to the characteristic of MS lesion which primarily affects the white matter the study enhances MS lesions and also segments other tissues by applying the probability map The proposed method is validated by comparing with the original MCMICO algorithm and an existing toolbox LPA The measures mostly demonstrate a great improvement of our method With introducing an atlas probability model as the priori knowledge the segmentation method effectively rejects the false positives After post-processing the proposed method further improves the results
Date of Award2017 Sep 6
Original languageEnglish
SupervisorChou-Ching Lin (Supervisor)

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