Learning-based image segmentation for IVUS images Raja Yalamanchili Computational Biomedicine Lab 1
Feb 24, 2016
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Learning-based image segmentation for IVUS images
Raja YalamanchiliComputational Biomedicine
Lab
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Intravascular Ultrasound (IVUS) imaging
Figure credits: Montana State University http://www.montana.edu/wwwai/imsd/diabetes/myocard.htm, Yale-New Haven Hospital. http://www.ynhh-healthlibrary.org, Normatem. http://www.normatem.com/vp.html
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Anatomy of Blood Vessel
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Problem Statement• Automatic segmentation of different
layers of a vessel to study characteristics of plaques and vessels– Lumen/Intima border–Media/Adventia border
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Significance• Manual segmentation of even one
frame is time consuming
• IVUS sequence consists of thousands of frames
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Lumen Media
Adventia
Challenges: Low Contrast
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Challenges: Image Appearance
Images acquired with 20MHz and 40MHz catheter frequency
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Challenges: Image Appearance (2)
Same image with different transformation parameters
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Challenges: Artifacts
A. Ringdown artifact
B. Guidewire artifact
C. Acoustic Shadowing
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Literature Review• Image-based methods
– Sonka et al. , Birgelen et al. , Zhang et al. (intensity and gradient information combined with Computational methods )
– Haas et al. , Luo et al. , Hui-Zhu et al. , Cardinal et al. , dos Santos Filho et al. (texture, statistical, temporal properties of images)
• RF-based methods– Nair et al. , Nasu et al. , Kawasaki et al. , O’ Malley et al. ,
Mendizabal-Ruiz et al.
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Limitations• Image-based methods rely on image
properties– Image appearance– artifacts
• No way to correct the segmentation result
• Difficult to create a training set that can include all variations
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Active learning methodSegmentation
Algorithm
ConfidenceMeasure
User Interaction
Update Segmentation Parameters
Final Result
Preliminary Result
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Thank you!