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.
Sun Young Park and Dustin Sargent, "Tumor propagation model using generalized hidden Markov model," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331G (Presented at SPIE Medical Imaging: February 14, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2254583.
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