3 March 2017 Models of temporal enhanced ultrasound data for prostate cancer diagnosis: the impact of time-series order
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Abstract
Recent studies have shown the value of Temporal Enhanced Ultrasound (TeUS) imaging for tissue characterization in transrectal ultrasound-guided prostate biopsies. Here, we present results of experiments designed to study the impact of temporal order of the data in TeUS signals. We assess the impact of variations in temporal order on the ability to automatically distinguish benign prostate-tissue from malignant tissue. We have previously used Hidden Markov Models (HMMs) to model TeUS data, as HMMs capture temporal order in time series. In the work presented here, we use HMMs to model malignant and benign tissues; the models are trained and tested on TeUS signals while introducing variation to their temporal order. We first model the signals in their original temporal order, followed by modeling the same signals under various time rearrangements. We compare the performance of these models for tissue characterization. Our results show that models trained over the original order-preserving signals perform statistically significantly better for distinguishing between malignant and benign tissues, than those trained on rearranged signals. The performance degrades as the amount of temporal-variation increases. Specifically, accuracy of tissue characterization decreases from 85% using models trained on original signals to 62% using models trained and tested on signals that are completely temporally-rearranged. These results indicate the importance of order in characterization of tissue malignancy from TeUS data.
Conference Presentation
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Layan Nahlawi, Caroline Goncalves, Farhad Imani, Mena Gaed, Jose A. Gomez, Madeleine Moussa, Eli Gibson, Aaron Fenster, Aaron D. Ward, Purang Abolmaesumi, Parvin Mousavi, Hagit Shatkay, "Models of temporal enhanced ultrasound data for prostate cancer diagnosis: the impact of time-series order", Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101351D (3 March 2017); doi: 10.1117/12.2255798; https://doi.org/10.1117/12.2255798
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