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Proceedings Paper

Tumor propagation model using generalized hidden Markov model
Author(s): Sun Young Park; Dustin Sargent
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Paper Abstract

Tumor tracking and progression analysis using medical images is a crucial task for physicians to provide accurate and efficient treatment plans, and monitor treatment response. Tumor progression is tracked by manual measurement of tumor growth performed by radiologists. Several methods have been proposed to automate these measurements with segmentation, but many current algorithms are confounded by attached organs and vessels. To address this problem, we present a new generalized tumor propagation model considering time-series prior images and local anatomical features using a Hierarchical Hidden Markov model (HMM) for tumor tracking. First, we apply the multi-atlas segmentation technique to identify organs/sub-organs using pre-labeled atlases. Second, we apply a semi-automatic direct 3D segmentation method to label the initial boundary between the lesion and neighboring structures. Third, we detect vessels in the ROI surrounding the lesion. Finally, we apply the propagation model with the labeled organs and vessels to accurately segment and measure the target lesion. The algorithm has been designed in a general way to be applicable to various body parts and modalities. In this paper, we evaluate the proposed algorithm on lung and lung nodule segmentation and tracking. We report the algorithm’s performance by comparing the longest diameter and nodule volumes using the FDA lung Phantom data and a clinical dataset.

Paper Details

Date Published: 24 February 2017
PDF: 8 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331G (24 February 2017); doi: 10.1117/12.2254583
Show Author Affiliations
Sun Young Park, Merge Healthcare Inc., an IBM Co. (United States)
Dustin Sargent, Merge Healthcare Inc., an IBM Co. (United States)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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