Classification of prostate cancer grades and T-stages based on tissue elasticity using medical image analysis

Shan Yang; Vladimir Jojic; Jun Lian; Ronald Chen; Hongtu Zhu; Ming C. Lin

doi:10.1007/978-3-319-46720-7_73

Classification of prostate cancer grades and T-stages based on tissue elasticity using medical image analysis

Shan Yang, Vladimir Jojic, Jun Lian, Ronald Chen, Hongtu Zhu, Ming C. Lin

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

2 Scopus citations

Abstract

In this paper,we study the correlation of tissue (i.e. prostate) elasticity with the spread and aggression of prostate cancers.We describe an improved,in-vivo method that estimates the individualized,relative tissue elasticity parameters directly from medical images. Although elasticity reconstruction,or elastograph,can be used to estimate tissue elasticity,it is less suited for in-vivo measurements or deeply-seated organs like prostate. We develop a non-invasive method to estimate tissue elasticity values based on pairs of medical images,using a finite-element based biomechanical model derived from an initial set of images,local displacements,and an optimization-based framework. We demonstrate the feasibility of a statistically-based multi-class learning method that classifies a clinical T-stage and Gleason score using the patient’s age and relative prostate elasticity values reconstructed from computed tomography (CT) images.

Original language	English (US)
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	627-635
Number of pages	9
ISBN (Print)	9783319467191
DOIs	https://doi.org/10.1007/978-3-319-46720-7_73
State	Published - 2016
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: Oct 21 2016 → Oct 21 2016

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

Conference

Conference	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	10/21/16 → 10/21/16

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-46720-7_73

Cite this

Yang, S., Jojic, V., Lian, J., Chen, R., Zhu, H., & Lin, M. C. (2016). Classification of prostate cancer grades and T-stages based on tissue elasticity using medical image analysis. 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. 627-635). (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_73

Classification of prostate cancer grades and T-stages based on tissue elasticity using medical image analysis. / Yang, Shan; Jojic, Vladimir; Lian, Jun 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. 627-635 (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

Yang, S, Jojic, V, Lian, J, Chen, R, Zhu, H & Lin, MC 2016, Classification of prostate cancer grades and T-stages based on tissue elasticity using medical image analysis. 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. 627-635, 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, 10/21/16. https://doi.org/10.1007/978-3-319-46720-7_73

Yang S, Jojic V, Lian J, Chen R, Zhu H, Lin MC. Classification of prostate cancer grades and T-stages based on tissue elasticity using medical image analysis. 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. 627-635. (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_73

Yang, Shan ; Jojic, Vladimir ; Lian, Jun et al. / Classification of prostate cancer grades and T-stages based on tissue elasticity using medical image analysis. 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. 627-635 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper,we study the correlation of tissue (i.e. prostate) elasticity with the spread and aggression of prostate cancers.We describe an improved,in-vivo method that estimates the individualized,relative tissue elasticity parameters directly from medical images. Although elasticity reconstruction,or elastograph,can be used to estimate tissue elasticity,it is less suited for in-vivo measurements or deeply-seated organs like prostate. We develop a non-invasive method to estimate tissue elasticity values based on pairs of medical images,using a finite-element based biomechanical model derived from an initial set of images,local displacements,and an optimization-based framework. We demonstrate the feasibility of a statistically-based multi-class learning method that classifies a clinical T-stage and Gleason score using the patient{\textquoteright}s age and relative prostate elasticity values reconstructed from computed tomography (CT) images.",

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AB - In this paper,we study the correlation of tissue (i.e. prostate) elasticity with the spread and aggression of prostate cancers.We describe an improved,in-vivo method that estimates the individualized,relative tissue elasticity parameters directly from medical images. Although elasticity reconstruction,or elastograph,can be used to estimate tissue elasticity,it is less suited for in-vivo measurements or deeply-seated organs like prostate. We develop a non-invasive method to estimate tissue elasticity values based on pairs of medical images,using a finite-element based biomechanical model derived from an initial set of images,local displacements,and an optimization-based framework. We demonstrate the feasibility of a statistically-based multi-class learning method that classifies a clinical T-stage and Gleason score using the patient’s age and relative prostate elasticity values reconstructed from computed tomography (CT) images.

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Classification of prostate cancer grades and T-stages based on tissue elasticity using medical image analysis

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