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Segmentatio n of Subcortical Sructures in M R Images

Segmentatio n of Subcortical Sructures in M R Images. VPA. Outline. Introduction MRI Segmentation. Subcortical structures. Challenging Problems . Low Contrast Shape & Topological Complexity Database Tools MRI visualization tool. Make traning set Generate ground truth.

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Segmentatio n of Subcortical Sructures in M R Images

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  1. Segmentation of Subcortical Sructuresin MR Images VPA

  2. Outline Introduction • MRI Segmentation. • Subcortical structures. • Challenging Problems. • Low Contrast • Shape & Topological Complexity • Database • Tools • MRI visualization tool. • Make traning set • Generate ground truth. • Segmentation Issue • Our Task • Results • Validation VPA

  3. IntroductionSubcortical Structures (BG) • Neurodegenerative diseases, such asAlzheimer and Parkinson. • Early detection requires thorough understanding of the chemical and anatomical changes in the brain. • Automatic segmentation methods to detect changes • Determine shape abnormalities and help radiologists to find out the functionals of different organs in neurological diseases VPA

  4. IntroductionSubcortical Structures (BG) VPA 4

  5. Challenging Problems Shape & Topological Complexity, Low Contrast • Low Contrast • Very similar intensity values for different tissues • Very closed positions of organs. • Multi pieced structured organs. VPA 5

  6. Database ASM – Siemens (Avanto) • T2 and PD sequence, dicom. • 18 normal patient • 1. 5 T • 512 x 448 dimension • 3 mm slice thickness • T1 sequence, dicom • 3 Patient • 1. 5 T • 512 x 448, dim. • 1 mm slice thickness VPA

  7. Tools Visualization VPA 7

  8. Tools Generate Training VPA 8

  9. Segmentation IssueOur Task • Use of curve evolution based segmentation methods • Region Based (Chan & Vese) • The use of shape priors • Embedding non parametric joint shape model into segmentation process. • The use of information, obtained from spatial position relation within the organs. • Embedding relative pose prior information of neighboring structures. VPA

  10. Segmentation Results • Putamen & Caudate Without Shape Knowledge With Joint Shape Knowledge With Shape Knowledge VPA

  11. Segmentation Validation VPA 11

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