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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

20.06.2017 | Original Article

Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations

verfasst von: Shekoofeh Azizi, Sharareh Bayat, Pingkun Yan, Amir Tahmasebi, Guy Nir, Jin Tae Kwak, Sheng Xu, Storey Wilson, Kenneth A. Iczkowski, M. Scott Lucia, Larry Goldenberg, Septimiu E. Salcudean, Peter A. Pinto, Bradford Wood, Purang Abolmaesumi, Parvin Mousavi

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 8/2017

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Abstract

Purpose

Temporal Enhanced Ultrasound (TeUS) has been proposed as a new paradigm for tissue characterization based on a sequence of ultrasound radio frequency (RF) data. We previously used TeUS to successfully address the problem of prostate cancer detection in the fusion biopsies.

Methods

In this paper, we use TeUS to address the problem of grading prostate cancer in a clinical study of 197 biopsy cores from 132 patients. Our method involves capturing high-level latent features of TeUS with a deep learning approach followed by distribution learning to cluster aggressive cancer in a biopsy core. In this hypothesis-generating study, we utilize deep learning based feature visualization as a means to obtain insight into the physical phenomenon governing the interaction of temporal ultrasound with tissue.

Results

Based on the evidence derived from our feature visualization, and the structure of tissue from digital pathology, we build a simulation framework for studying the physical phenomenon underlying TeUS-based tissue characterization.

Conclusion

Results from simulation and feature visualization corroborated with the hypothesis that micro-vibrations of tissue microstructure, captured by low-frequency spectral features of TeUS, can be used for detection of prostate cancer.

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Titel: Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations
verfasst von: Shekoofeh Azizi
Sharareh Bayat
Pingkun Yan
Amir Tahmasebi
Guy Nir
Jin Tae Kwak
Sheng Xu
Storey Wilson
Kenneth A. Iczkowski
M. Scott Lucia
Larry Goldenberg
Septimiu E. Salcudean
Peter A. Pinto
Bradford Wood
Purang Abolmaesumi
Parvin Mousavi
Publikationsdatum: 20.06.2017
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 8/2017
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-017-1627-0

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Abstract

Purpose

Methods

Results

Conclusion

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