segmentation of 3d tubular structures paul hernandez-herrera computational biomedicine lab advisor:...
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![Page 1: Segmentation of 3D Tubular Structures Paul Hernandez-Herrera Computational Biomedicine Lab Advisor: Ioannis A. Kakadiaris and Manos Papadakis 1](https://reader035.vdocuments.us/reader035/viewer/2022062408/56649f285503460f94c40a67/html5/thumbnails/1.jpg)
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Segmentation of 3D Tubular Structures
Paul Hernandez-HerreraComputational Biomedicine Lab
Advisor: Ioannis A. Kakadiaris and Manos Papadakis
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Motivation
• Tubular structures appear in biomedical images– Neuron– Vessels– Coronary arteries– Airways
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Challenges• Size
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• Intensity
Challenges
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• Noise
Challenges
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• Contrast
Challenges
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1. 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
Thesis Objectives
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Thesis Objectives
2. Develop a centerline algorithm able to– Correctly extract the morphology
• Handle overlapping structures• connect gaps
– Fast extraction
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PipelineInput:3D image stackRadius
Step 1:Background
voxels detection
Step 2:Feature
extraction
Step 3:Background
enhancement
Step 4:Segmentation
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Segmentation as one-class classification
Input:3D imageRadius
Detect voxels in background
Voxels with unknown label
Train a model(Cost function)
Feature vectors
Get cost value
Accepted as Background
Rejected as Background
These are foreground
voxels
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Step1: Background voxel detection
• Compute the Laplacian of the 3D image• The output has the following properties
1. Negative values in the foreground2. Value close to zero in the boundary3. It is positive near but outside the TS4. Ringing (positive and negative) in the background
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Step 2: Feature extraction• Feature vector
Eigenvalues of Hessian matrix
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Step 3: Cost function• Approximate feature vectors distribution
for background voxels• Normalize the distribution • Smooth the normalized distribution
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Step 3: Background enhancement
Input image Enhanced image
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Step 4: Segmentation
Enhanced image Segmentation
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Results: Multiphoton
Input Segmentation
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Results: Confocal
Input Segmentation
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Results: Brain vessels
Input Segmentation
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Ongoing work• Automatic radius estimation• Allow the proposed method to handle
any number of features• Centerline extraction
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Thanks
Thanks for your attention
QUESTIONS?