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Proceedings Paper

Models of temporal enhanced ultrasound data for prostate cancer diagnosis: the impact of time-series order
Author(s): 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
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Paper 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.

Paper Details

Date Published: 3 March 2017
PDF: 7 pages
Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101351D (3 March 2017); doi: 10.1117/12.2255798
Show Author Affiliations
Layan Nahlawi, Queen's Univ. (Canada)
Caroline Goncalves, Queen's Univ. (Canada)
Farhad Imani, The Univ. of British Columbia (Canada)
Mena Gaed, Western Univ. (Canada)
Jose A. Gomez, Western Univ. (Canada)
Madeleine Moussa, Western Univ. (Canada)
Eli Gibson, Univ. College London (United Kingdom)
Aaron Fenster, Western Univ. (Canada)
Aaron D. Ward, Western Univ. (Canada)
Purang Abolmaesumi, The Univ. of British Columbia (Canada)
Parvin Mousavi, Queen's Univ. (Canada)
Hagit Shatkay, Queen's Univ. (Canada)
Univ. of Delaware (United States)


Published in SPIE Proceedings Vol. 10135:
Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster; Baowei Fei, Editor(s)

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