Fuzzy-Based Extraction of Vascular Structures from Time-of-Flight MR - - PowerPoint PPT Presentation

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Fuzzy-Based Extraction of Vascular Structures from Time-of-Flight MR Images

N.D. Forkert1, D. Säring1, K. Wenzel2, T. Illies2, J. Fiehler2, H. Handels1

1 Department of Medical Informatics 2 Department of Diagnostic and Interventional Neuroradiology

University Medical Center Hamburg-Eppendorf

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Cerebral Vascular Diseases

• ! The stroke is third most common reason

for death in Europe

• ! 80% are caused by a ischemia

(lack of blood supply)

• ! In 20 % are caused by hemorrhage
• ! Hemorrhages are mostly caused by a

rupture of a malformed blood vessel.

• ! Examples for malformations:

–! Aneurysms –! Arteriovenous malformations (AVM)

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Therapy of Cerebral Vessel Malformations

• ! Neurosurgical Resection
• ! Endovascular Treatment

–! Embolisation –! Coiling

• ! Radiosurgery
• ! Decision of treatment mode(s)

–! Size –! Location –! …

Exact knowledge about the individual anatomy of the vascular system is needed for risk estimation and therapy

Image of AVM surgery

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3D-Time-of-Flight (TOF) MRA

• ! 3D Time-of-Flight technique is
• ften used for visualization of

the vascular system

–! 3T MR Scanner (Siemens)

• ! Matrix: 384 x 512 *
• ! Size: 0.47 x 0.47 mm *

(* = typical values)

• ! Slice thickness: 0.5 mm
• ! 156 Slices
• ! Anatomical Information
• ! Improved blood to background

contrast

3D TOF-MRA with improved blood to background contrast

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Artifact Reduction

• ! Multi-Slab technology

acquisition of TOF image sequences

• !Reduction of the

amplitude in overlapping regions (Slab Boundary Artifact)

• ! Reduction of the Slab

Boundary Artifact using histogram matching*

* Kholmovski et al. Correction of Slab Boundary Artifact Using Histogramm Matching. J Magn Reson Imaging. 2002;15:610-617. MIP-Visualization of a TOF image sequence after Slab Boundary Artifact reduction MIP-Visualization of a TOF image sequence

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Skull Stripping

• ! Drawback of TOF image

data: Non-cerebral tissues like fat, bone marrow and eyes are represented by intensities similar to the vessels

• ! hindered segmentation of

blood vessels

• ! Skull Stripping using a

graph-based approach*

Volume Rendered TOF image before (top) and after (bottom) skull stripping

*Forkert et al. Automatic Brain Segmentation in Time-of-Flight MRA Images Methods of Information in Medicine, 48(5), 2009 (in press)

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Vessel Segmentation – State of the Art

• ! Intensity-based approaches:

–! Typical intensity distributions –! e.g. Z-Buffer-Segmentation 1 (global threshold) –! Problem: small vessels are

• ften not detected
• ! Model-based approaches:

–! Typical vessel morpholgy –! e.g. Vesselness-Filter 2 –! Problem: vessel malformations are often not detected

1 Chapman et al., Intracranial vessel segmentation from time-of-flight MRA using preprocessing of the MIP

Z-Buffer, Medical Image Analysis 8 (2004), 113-126.

2 Sato et al. Three-dimensional multi-scale Line Filter for Segmentation and Visualization of Curvelinear Structures

in Medical Images. Med Image Anal. 1998;2(2):143-168. TOF-Slice Vesselness-Result

→ Combination of intensity- and shape-information

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Fuzzy Vessel Segmentation

• 1. Preprocessing
• 2. Fuzzy Inference System
• 3. Fuzzy Extraction

Fuzzy Inference System Fuzzy- Connectedness Preprocessing

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• 1. Preprocessing
• ! Computation of the Vesselness-

Image*

–! A value of vesselness measure is assigned to every voxel based on eigen values of the Hessian matrix –! Benefit: Enhanced display of the vascular structures, especially small vessels –! Drawback: Vascular malformations are not detected

• ! Computation of the Maximum-

Image

–! The maximal intensity within a defined 3D neighborhood is assigned to every voxel

*Sato et al. Three-dimensional multi-scale Line Filter for Segmentation and Visualization

• f Curvelinear Structures in Medical Images. Med Image Anal. 1998;2(2):143-168.

Vesselness- Image TOF-Slice Maximum- Image

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• 2. Fuzzy Inference System
• ! Combination of the images using fuzzy Inference system
• ! Benefits:

–! Non-linear combination parameters –! Inclusion of uncertain knowledge –! Well explored and broad utilization in control engineering

• ! Steps:

–! Fuzzyfication –! Inferenz

»! Aggregation »! Implication »! Accumulation

–! Defuzzyfication

Fuzzy Inference System

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• 2. Fuzzy Inference System
• ! Fuzzyfication:

–! Sharp input value specifies a degree of membership of each fuzzy set –! 3 Fuzzy-Sets (low, medium, high) –! Functions for Fuzzy-sets are generated automatically based on empirical knowledge –! Example: Intensity value 210 leads to a degree of 0.8 to medium and 0.2 to high

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• 2. Fuzzy Inference System
• ! Fuzzy Inference:

–! 27 rules (3 inputs with 3 linguistic terms) –! 5 conclusions for vessel probability: very low, low, medium, high, very high –! Main idea for definition of the rule base:

Weight the response of the vesselness filter stronger if the maximum filter responds a low value, whereas the TOF-input is weighted stronger otherwise.

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• 2. Fuzzy Inference System
• ! Example for a rule:

If the TOF-Intensity is „medium“ and the vesselness-measure „high“ and the Maximum-Filter-value „low“ then the vessel probability is „high“.

• ! Aggregation:

–! combination the degrees of membership of the premise parts of a rule to one value for the whole premise –! Minimum-Operators –! Example: »! 0.9 for “TOF Intensity is medium” »! 0.7 for “Vesselness-Measure is high” »! 0.7 for “Maximumfilter-Value is low” –! Degree for the premise: min(0.9, 0.7, 0.7) = 0.7

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• 2. Fuzzy Inference System
• ! Example for a rule:

If the TOF-Intensity is „medium“ and the vesselness-measure „high“ and the Maximum-Filter-value „low“ then the vessel probability is „high“.

• ! Aggregation
• ! Implication:

–! Determinition of the membership degree of the conclusion –! cutting the fuzzy set

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• 2. Fuzzy Inference System
• ! Example for a rule:

If the TOF-Intensity is „medium“ and the vesselness-measure „high“ and the Maximum-Filter-value „low“ then the vessel probability is „high“.

• ! Aggregation
• ! Implication
• ! Accumulation:

–! Accumulation of the single results of all rules –! Maximum-Operator

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• 2. Fuzzy Inference System
• ! Example for a rule:

If the TOF-Intensity is „medium“ and the vesselness-measure „high“ and the Maximum-Filter-value „low“ then the vessel probability is „high“.

• ! Aggregation
• ! Implication
• ! Accumulation
• ! Defuzzyfication

–! Calculation of a sharp output value –! Center of gravity method

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• 3. Fuzzy Extraction
• ! Fuzzy-Parameter-Image:

–! Small Vessels as well as malformation are enhanced

• ! Vessel extraction:

–! Global thresholding –! Connected component analysis –! Mean and standard deviation computation of the fuzzy values of each component –! Fuzzy-Connectedness Approach* –! Result: Extracted vascular system

*Udupa et al.: Fuzzy Connectedness and Object Denition: Theory, Algorithms, and Applications in Image Segmentation. Graphical Models. 1996;58(3):246-261. Fuzzy-Parameter-Image

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Results

• ! 17 datasets of patients with an arteriovenous malformation
• ! Manual segmentation:

–! Semi-automatic segmentation –! Volume-Growing –! Manual correction in orthogonal views –! Time requirements: 8-12 hours –! Performed by neuroradiologists

• ! Automatic segmentations:

–! Z-Buffer Segmentation, Time: ~5 min –! Fuzzy Segmentation: ~30 min

• ! Evaluation

–! Dice-Value –! Kappa-Value

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Results

• ! Mean Dice-Value

–! Z-Buffer Segmentation: 0.577 –! Fuzzy Segmentation: 0.742

• ! Mean Kappa-Value

–! Z-Buffer Segmentation: 0,567 –! Fuzzy Segmentation: 0,775

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Results

• ! The quantitative results

depend on the size of the AVM-nidus

• ! One dataset with two manual

segmentations from different experts

• !Inter-Observer Comparison

Dice-values in dependency to the nidus size

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Results (Inter-Observer-Comparison)

• ! Mean Dice-Value

–! Inter-Observer Comparison: 0,83 –! Z-Buffer Segmentation: 0.695 –! Fuzzy Segmentation: 0,815

• ! Mean Kappa-Value

–! Inter-Observer Comparison: 0.84 –! Z-Buffer Segmentation: 0.755 –! Fuzzy Segmentation: 0,845

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Results

• ! Section of a TOF slice

Manual Segmentations Z-Buffer Segmentation Fuzzy Segmentation

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Results

• ! Surface Models

Manual segmentation Z-Buffer segmentation Fuzzy segmentation

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Summary and Discussion

• ! A fuzzy based method for automatic extraction of the vascular

system from 3D TOF image sequences was presented

–! Preprocessing –! Fuzzy combination of intensity- and shape-information –! Vessel extraction using Fuzzy-Connectedness

• ! Benefits:

–! Malformations are detected –! Robust results in the area of the Inter-Observer-Comparison –! Better results than the Z-Buffer segmentation for every dataset

• ! For further evaluation more manual as well as automatic

segmentations are required

• ! The approach presented allows a robust and automatic

segmentation of the vascular system

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Thank you for your attention

This work is supported by German Research Foundation (DFG, HA 2355/10-1)