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Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR-only Radiotherapy Planning Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Fast Vessel Segmentation and Tracking in Ultra High-Frequency Ultrasound Images

Authors : Tejas Sudharshan Mathai, Lingbo Jin, Vijay Gorantla, John Galeotti

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Publisher: Springer International Publishing

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Abstract

Ultra High Frequency Ultrasound (UHFUS) enables the visualization of highly deformable small and medium vessels in the hand. Intricate vessel-based measurements, such as intimal wall thickness and vessel wall compliance, require sub-millimeter vessel tracking between B-scans. Our fast GPU-based approach combines the advantages of local phase analysis, a distance-regularized level set, and an Extended Kalman Filter (EKF), to rapidly segment and track the deforming vessel contour. We validated on 35 UHFUS sequences of vessels in the hand, and we show the transferability of the approach to 5 more diverse datasets acquired by a traditional High Frequency Ultrasound (HFUS) machine. To the best of our knowledge, this is the first algorithm capable of rapidly segmenting and tracking deformable vessel contours in 2D UHFUS images. It is also the fastest and most accurate system for 2D HFUS images.

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Metadata
Title
Fast Vessel Segmentation and Tracking in Ultra High-Frequency Ultrasound Images
Authors
Tejas Sudharshan Mathai
Lingbo Jin
Vijay Gorantla
John Galeotti
Copyright Year
2018
Book
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Print ISBN: 978-3-030-00936-6

Electronic ISBN: 978-3-030-00937-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00937-3_85

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