Nahlawi, L;
Goncalves, C;
Imani, F;
Gaed, M;
Gomez, JA;
Moussa, M;
Gibson, E;
... Shatkay, H; + view all
(2017)
Models of Temporal Enhanced Ultrasound Data for Prostate Cancer Diagnosis: The Impact of Time-Series Order.
In: Webster, RJ and Fei, B, (eds.)
Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling.
(pp. 101351D1-101351D7).
Society of Photo-Optical Instrumentation Engineers (SPIE)
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Models of Temporal Enhanced Ultrasound Data for Prostate Cancer Diagnosis: The Impact of Time-Series Order |
| Event: | SPIE Medical Imaging 2017 |
| Location: | Orlando, Florida, United States |
| Dates: | 11 February 2017 - 16 February 2017 |
| ISBN-13: | 9781510607163 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1117/12.2255798 |
| Publisher version: | http://doi.org/10.1117/12.2255798 |
| Language: | English |
| Additional information: | © 2017 SPIE. This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Physical Sciences, Life Sciences & Biomedicine, Optics, Radiology, Nuclear Medicine & Medical Imaging, Hidden Markov Models, Time-series, Ultrasound, Prostate Cancer, Tissue Characterization, Temporal Order |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/1571885 |
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