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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?