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. 2018 Aug;13(8):1201-1209.
doi: 10.1007/s11548-018-1749-z. Epub 2018 Mar 27.

Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy

Shekoofeh Azizi  1 , Nathan Van Woudenberg  2 , Samira Sojoudi  2 , Ming Li  3 , Sheng Xu  3 , Emran M Abu Anas  4 , Pingkun Yan  5 , Amir Tahmasebi  6 , Jin Tae Kwak  7 , Baris Turkbey  3 , Peter Choyke  3 , Peter Pinto  3 , Bradford Wood  3 , Parvin Mousavi  8 , Purang Abolmaesumi  2
Affiliations

Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy

Shekoofeh Azizi et al. Int J Comput Assist Radiol Surg. 2018 Aug.

Abstract

Purpose: We have previously proposed temporal enhanced ultrasound (TeUS) as a new paradigm for tissue characterization. TeUS is based on analyzing a sequence of ultrasound data with deep learning and has been demonstrated to be successful for detection of cancer in ultrasound-guided prostate biopsy. Our aim is to enable the dissemination of this technology to the community for large-scale clinical validation.

Methods: In this paper, we present a unified software framework demonstrating near-real-time analysis of ultrasound data stream using a deep learning solution. The system integrates ultrasound imaging hardware, visualization and a deep learning back-end to build an accessible, flexible and robust platform. A client-server approach is used in order to run computationally expensive algorithms in parallel. We demonstrate the efficacy of the framework using two applications as case studies. First, we show that prostate cancer detection using near-real-time analysis of RF and B-mode TeUS data and deep learning is feasible. Second, we present real-time segmentation of ultrasound prostate data using an integrated deep learning solution.

Results: The system is evaluated for cancer detection accuracy on ultrasound data obtained from a large clinical study with 255 biopsy cores from 157 subjects. It is further assessed with an independent dataset with 21 biopsy targets from six subjects. In the first study, we achieve area under the curve, sensitivity, specificity and accuracy of 0.94, 0.77, 0.94 and 0.92, respectively, for the detection of prostate cancer. In the second study, we achieve an AUC of 0.85.

Conclusion: Our results suggest that TeUS-guided biopsy can be potentially effective for the detection of prostate cancer.

Keywords: 3D slicer; Prostate cancer; Real-time biopsy guidance; Temporal enhanced ultrasound.

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Conflict of interest statement

Conflict of interest The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
TeUS: changes in backscattered time series of ultrasound, captured from a point in tissue (red dot), are analyzed using machine learning to characterize tissue
Fig. 2
Fig. 2
Overview of the biopsy guidance system. The three steps in the guidance workflow are volume acquisition, classification and guidance. A client–server approach allows for simultaneous and real-time execution of computationally expensive algorithms including TeUS data classification and prostate boundary segmentation
Fig. 3
Fig. 3
The software system has a three-tiered architecture. Ovals represent processing elements, while arrows show the direction of data flow. In the US-machine layer, PLUS is responsible for US data acquisition and communicates with the TeUS-client via the OpenIGTLink protocol. The TeUS-client layer includes TeUS guidance, an extension module within the 3D Slicer framework. The TeUS-server layer is responsible for the simultaneous and real-time execution of computationally expensive algorithms and communicates with TeUS-client via the OpenIGTLink protocol
Fig. 4
Fig. 4
Guidance interface implemented as part of a 3D Slicer module: cancer likelihood map is overlaid on B-mode ultrasound images. Red indicates predicted labels as cancer, and blue indicates predicted benign regions. The boundary of the segmented prostate is shown with white, and the green circle is centered around the target location which is shown in green dot
See this image and copyright information in PMC

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