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MS Lesion Visualization Assisted Segmentation Daniel Biediger COSC 6397 – Scientific Visualization
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Multiple Sclerosis Autoimmune disease of the central nervous system Affects 2.5 million people globally, ~100 per 100,000 Damage to the insulating myelin sheaths around brain cells Appears as a range of cognitive and physical disability Progresses at different rates with remission and relapse 2 Myelin: ~40% water, ~40-50% lipids
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Multiple Sclerosis Identified in-vivo with MRI images Normally 3 tissues: GM, WM, CSF (SG, SB, LCR) Lesion appear hyper- or hypo-intense (bright or dark) – Reflects a difference in relaxation times for lipids/water – Detected as an outlier to the normal WM – FLAIR MRI (Fluid Attenuated Inversion Recovery) best 3
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MS Detection Challenges WM Lesion detection is a challenge – Overlap in intensity between tissue classes – 3D brain structure (sulcus, ventricles, multiple tissues) – Partial Volume Effects (PVE) – Artifacts from fluid/patient motion, tumors, etc – Random noise, bias field, sun spots, etc Requires an expert to identify lesions – Tissue identification (T1-weighted MRI) – Lesion detection (FLAIR MRI) – Time consuming, difficult (multi-modality), and subjective 4
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MS Lesion Appearance in Flair 5 This? or Is this?
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Result Comparison 6 Under Segmentation Correct Segmentation Over Segmentation DICE = 0.65 FLAIR MRIBase MethodExpertMe Results
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Project Timeline 7
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Questions 8 ?
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