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Segmentation of 3D Tubular Structures

Segmentation of 3D Tubular Structures. Paul Hernandez-Herrera Computational Biomedicine Lab Advisor: Ioannis A. Kakadiaris and Manos Papadakis. Motivation. Tubular structures appear in biomedical images Neuron Vessels Coronary arteries Airways. Challenges. Size. Challenges.

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Segmentation of 3D Tubular Structures

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  1. Segmentation of 3D Tubular Structures Paul Hernandez-Herrera Computational Biomedicine Lab Advisor: Ioannis A. Kakadiaris and Manos Papadakis

  2. Motivation • Tubular structures appear in biomedical images • Neuron • Vessels • Coronary arteries • Airways

  3. Challenges • Size

  4. Challenges • Intensity

  5. Challenges • Noise

  6. Challenges • Contrast

  7. Thesis Objectives • Develop a binary segmentation algorithm able to • handle different sizes • work with any acquisition modality • deal with noise in the image • handle anisotropic images • do a fast segmentation • have minimum or null user interaction

  8. Thesis Objectives • Develop a centerline algorithm able to • Correctly extract the morphology • Handle overlapping structures • connect gaps • Fast extraction

  9. Pipeline Step 2: Feature extraction Input: 3D image stack Radius Step 1: Background voxels detection Step 4: Segmentation Step 3: Background enhancement

  10. Segmentation as one-class classification Detect voxels in background Train a model (Cost function) Input: 3D image Radius Accepted as Background Feature vectors Get cost value Rejected as Background Voxelswith unknown label These are foreground voxels

  11. Step1: Background voxel detection • Compute the Laplacian of the 3D image • The output has the following properties • Negative values in the foreground • Value close to zero in the boundary • It is positive near but outside the TS • Ringing (positive and negative) in the background

  12. Step 2: Feature extraction • Feature vector • Eigenvalues of Hessian matrix

  13. Step 3: Cost function • Approximate feature vectors distribution for background voxels • Normalize the distribution • Smooth the normalized distribution

  14. Step 3: Background enhancement Input image Enhanced image

  15. Step 4: Segmentation Enhanced image Segmentation

  16. Results: Multiphoton Input Segmentation

  17. Results: Confocal Input Segmentation

  18. Results: Brain vessels Input Segmentation

  19. Ongoing work • Automatic radius estimation • Allow the proposed method to handle any number of features • Centerline extraction

  20. Thanks Thanks for your attention QUESTIONS?

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