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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!