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Learning-based image segmentation for IVUS images

Learning-based image segmentation for IVUS images. Raja Yalamanchili Computational Biomedicine Lab. Intravascular Ultrasound (IVUS) imaging. Figure credits:

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Learning-based image segmentation for IVUS images

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  1. Learning-based image segmentation for IVUS images Raja Yalamanchili Computational Biomedicine Lab

  2. 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

  3. Anatomy of Blood Vessel

  4. Problem Statement • Automatic segmentation of different layers of a vessel to study characteristics of plaques and vessels • Lumen/Intima border • Media/Adventia border

  5. Significance • Manual segmentation of even one frame is time consuming • IVUS sequence consists of thousands of frames

  6. Challenges: Low Contrast Lumen Media Adventia

  7. Challenges: Image Appearance Images acquired with 20MHz and 40MHz catheter frequency

  8. Challenges: Image Appearance (2) Same image with different transformation parameters

  9. Challenges: Artifacts • Ringdown artifact • Guidewire artifact • Acoustic Shadowing

  10. Literature Review • Image-based methods • Sonkaet al. , Birgelenet al. , Zhang et al. (intensity and gradient information combined with Computational methods ) • Haas et al. , Luoet al. , Hui-Zhu et al. , Cardinal et al. , dos Santos Filhoet 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.

  11. 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

  12. Active learning method Segmentation Algorithm Preliminary Result User Interaction Update Segmentation Parameters Confidence Measure Final Result

  13. Thank you!

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