Tumor propagation model using generalized hidden Markov model

Sun Young Park; Dustin Sargent

doi:10.1117/12.2254583

24 February 2017 Tumor propagation model using generalized hidden Markov model

Sun Young Park, Dustin Sargent

Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 101331G (2017) https://doi.org/10.1117/12.2254583
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Sun Young Park and Dustin Sargent "Tumor propagation model using generalized hidden Markov model", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331G (24 February 2017); https://doi.org/10.1117/12.2254583

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