期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 37, 期 12, 页码 2695-2703出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2018.2849959
关键词
Temporal enhanced ultrasound; deep learning; recurrent neural network; long short-termmemory; prostate cancer; cancer detection
类别
资金
- Natural Sciences and Engineering Research Council of Canada
- Philips Research North America, Cambridge, MA, USA
- NATIONAL CANCER INSTITUTE [ZIABC010655] Funding Source: NIH RePORTER
- CLINICAL CENTER [ZIACL040015] Funding Source: NIH RePORTER
Temporal enhanced ultrasound (TeUS), comprising the analysis of variations in backscattered signals from a tissue over a sequence of ultrasound frames, has been previously proposed as a new paradigm for tissue characterization. In this paper, we propose to use deep recurrent neural networks (RNN) to explicitly model the temporal information in TeUS. By investigating several RNN models, we demonstrate that long short-term memory (LSTM) networks achieve the highest accuracyin separating cancer from benign tissue in the prostate. We also present algorithms for in-depth analysis of LSTM networks. Our in vivo study includes data from 255 prostate biopsy cores of 157 patients. We achieve area under the curve, sensitivity, specificity, and accuracy of 0.96, 0.76, 0.98, and 0.93, respectively. Our result suggests that temporal modeling of TeUS using RNN can significantly improve cancer detection accuracy over previously presented works.
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