Classifying cancer grades using temporal ultrasound for transrectal prostate biopsy

Shekoofeh Azizi; Farhad Imani; Jin Tae Kwak; Amir Tahmasebi; Sheng Xu; Pingkun Yan; Jochen Kruecker; Baris Turkbey; Peter Choyke; Peter Pinto; Bradford Wood; Parvin Mousavi; Purang Abolmaesumi

doi:10.1007/978-3-319-46720-7_76

Classifying cancer grades using temporal ultrasound for transrectal prostate biopsy

Shekoofeh Azizi^*
, Farhad Imani
, Jin Tae Kwak
, Amir Tahmasebi
, Sheng Xu
, Pingkun Yan
, Jochen Kruecker
, Baris Turkbey
, Peter Choyke
, Peter Pinto
, Bradford Wood
, Parvin Mousavi
, Purang Abolmaesumi

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Citations (Scopus)

Abstract

We propose a cancer grading approach for transrectal ultrasound-guided prostate biopsy based on analysis of temporal ultrasound signals. Histopathological grading of prostate cancer reports the statistics of cancer distribution in a biopsy core. We propose a coarseto- fine classification approach,similar to histopathology reporting,that uses statistical analysis and deep learning to determine the distribution of aggressive cancer in ultrasound image regions surrounding a biopsy target. Our approach consists of two steps; in the first step,we learn high-level latent features that maximally differentiate benign from cancerous tissue. In the second step,we model the statistical distribution of prostate cancer grades in the space of latent features. In a study with 197 biopsy cores from 132 subjects,our approach can effectively separate clinically significant disease from low-grade tumors and benign tissue. Further,we achieve the area under the curve of 0.8 for separating aggressive cancer from benign tissue in large tumors.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
Editors	Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal
Publisher	Springer Verlag
Pages	653-661
Number of pages	9
ISBN (Print)	9783319467191
DOIs	https://doi.org/10.1007/978-3-319-46720-7_76
Publication status	Published - 2016
Externally published	Yes
Event	1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: 2016 Oct 21 → 2016 Oct 21

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9900 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Country/Territory	Greece
City	Athens
Period	16/10/21 → 16/10/21

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Cancer grading
Deep belief network
Gaussian mixture model
Temporal ultrasound

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-46720-7_76

Cite this

Azizi, S., Imani, F., Kwak, J. T., Tahmasebi, A., Xu, S., Yan, P., Kruecker, J., Turkbey, B., Choyke, P., Pinto, P., Wood, B., Mousavi, P., & Abolmaesumi, P. (2016). Classifying cancer grades using temporal ultrasound for transrectal prostate biopsy. In S. Ourselin, L. Joskowicz, M. R. Sabuncu, W. Wells, & G. Unal (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 653-661). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_76

Classifying cancer grades using temporal ultrasound for transrectal prostate biopsy. / Azizi, Shekoofeh; Imani, Farhad; Kwak, Jin Tae et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. ed. / Sebastian Ourselin; Leo Joskowicz; Mert R. Sabuncu; William Wells; Gozde Unal. Springer Verlag, 2016. p. 653-661 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Azizi, S, Imani, F, Kwak, JT, Tahmasebi, A, Xu, S, Yan, P, Kruecker, J, Turkbey, B, Choyke, P, Pinto, P, Wood, B, Mousavi, P & Abolmaesumi, P 2016, Classifying cancer grades using temporal ultrasound for transrectal prostate biopsy. in S Ourselin, L Joskowicz, MR Sabuncu, W Wells & G Unal (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9900 LNCS, Springer Verlag, pp. 653-661, 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 16/10/21. https://doi.org/10.1007/978-3-319-46720-7_76

Azizi S, Imani F, Kwak JT, Tahmasebi A, Xu S, Yan P et al. Classifying cancer grades using temporal ultrasound for transrectal prostate biopsy. In Ourselin S, Joskowicz L, Sabuncu MR, Wells W, Unal G, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer Verlag. 2016. p. 653-661. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46720-7_76

Azizi, Shekoofeh ; Imani, Farhad ; Kwak, Jin Tae et al. / Classifying cancer grades using temporal ultrasound for transrectal prostate biopsy. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. editor / Sebastian Ourselin ; Leo Joskowicz ; Mert R. Sabuncu ; William Wells ; Gozde Unal. Springer Verlag, 2016. pp. 653-661 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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AU - Yan, Pingkun

AU - Kruecker, Jochen

AU - Turkbey, Baris

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AU - Mousavi, Parvin

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ER -

Classifying cancer grades using temporal ultrasound for transrectal prostate biopsy

Abstract

Publication series

Other

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this