A 2.5D deep learning-based approach for prostate cancer detection on T2-weighted magnetic resonance imaging

Ruba Alkadi; Ayman El-Baz; Fatma Taher; Naoufel Werghi

doi:10.1007/978-3-030-11018-5_66

A 2.5D deep learning-based approach for prostate cancer detection on T2-weighted magnetic resonance imaging

Ruba Alkadi
, Ayman El-Baz
, Fatma Taher
, Naoufel Werghi

Electrical Engineering

University of Louisville

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

17 Scopus citations

Abstract

In this paper, we propose a fully automatic magnetic resonance image (MRI)-based computer aided diagnosis (CAD) system which simultaneously performs both prostate segmentation and prostate cancer diagnosis. The system utilizes a deep-learning approach to extract high-level features from raw T2-weighted MR volumes. Features are then remapped to the original input to assign a predicted label to each pixel. In the same context, we propose a 2.5D approach which exploits 3D spatial information without a compromise in computational cost. The system is evaluated on a public dataset. Preliminary results demonstrate that our approach outperforms current state-of-the-art in both prostate segmentation and cancer diagnosis.

Original language	British English
Title of host publication	Computer Vision – ECCV 2018 Workshops, Proceedings
Editors	Laura Leal-Taixé, Stefan Roth
Publisher	Springer Verlag
Pages	734-739
Number of pages	6
ISBN (Print)	9783030110178
DOIs	https://doi.org/10.1007/978-3-030-11018-5_66
State	Published - 2019
Event	15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany Duration: 8 Sep 2018 → 14 Sep 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11132 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th European Conference on Computer Vision, ECCV 2018
Country/Territory	Germany
City	Munich
Period	8/09/18 → 14/09/18

UN SDGs

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

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-030-11018-5_66

OpenUrl availability

Full text

Cite this

Alkadi, R., El-Baz, A., Taher, F., & Werghi, N. (2019). A 2.5D deep learning-based approach for prostate cancer detection on T2-weighted magnetic resonance imaging. In L. Leal-Taixé, & S. Roth (Eds.), Computer Vision – ECCV 2018 Workshops, Proceedings (pp. 734-739). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11132 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-11018-5_66

Alkadi, Ruba ; El-Baz, Ayman ; Taher, Fatma et al. / A 2.5D deep learning-based approach for prostate cancer detection on T2-weighted magnetic resonance imaging. Computer Vision – ECCV 2018 Workshops, Proceedings. editor / Laura Leal-Taixé ; Stefan Roth. Springer Verlag, 2019. pp. 734-739 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{64ceab9818f147ccb74e035ae058c6bc,

title = "A 2.5D deep learning-based approach for prostate cancer detection on T2-weighted magnetic resonance imaging",

abstract = "In this paper, we propose a fully automatic magnetic resonance image (MRI)-based computer aided diagnosis (CAD) system which simultaneously performs both prostate segmentation and prostate cancer diagnosis. The system utilizes a deep-learning approach to extract high-level features from raw T2-weighted MR volumes. Features are then remapped to the original input to assign a predicted label to each pixel. In the same context, we propose a 2.5D approach which exploits 3D spatial information without a compromise in computational cost. The system is evaluated on a public dataset. Preliminary results demonstrate that our approach outperforms current state-of-the-art in both prostate segmentation and cancer diagnosis.",

author = "Ruba Alkadi and Ayman El-Baz and Fatma Taher and Naoufel Werghi",

note = "Funding Information: Acknowledgement. This work is supported by a research grant from Al-Jalila foundation Ref: AJF-201616. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",

year = "2019",

doi = "10.1007/978-3-030-11018-5\_66",

language = "British English",

isbn = "9783030110178",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "734--739",

editor = "Laura Leal-Taix{\'e} and Stefan Roth",

booktitle = "Computer Vision – ECCV 2018 Workshops, Proceedings",

address = "Germany",

}

Alkadi, R, El-Baz, A, Taher, F & Werghi, N 2019, A 2.5D deep learning-based approach for prostate cancer detection on T2-weighted magnetic resonance imaging. in L Leal-Taixé & S Roth (eds), Computer Vision – ECCV 2018 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11132 LNCS, Springer Verlag, pp. 734-739, 15th European Conference on Computer Vision, ECCV 2018, Munich, Germany, 8/09/18. https://doi.org/10.1007/978-3-030-11018-5_66

A 2.5D deep learning-based approach for prostate cancer detection on T2-weighted magnetic resonance imaging. / Alkadi, Ruba; El-Baz, Ayman; Taher, Fatma et al.
Computer Vision – ECCV 2018 Workshops, Proceedings. ed. / Laura Leal-Taixé; Stefan Roth. Springer Verlag, 2019. p. 734-739 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11132 LNCS).

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

TY - GEN

T1 - A 2.5D deep learning-based approach for prostate cancer detection on T2-weighted magnetic resonance imaging

AU - Alkadi, Ruba

AU - El-Baz, Ayman

AU - Taher, Fatma

AU - Werghi, Naoufel

PY - 2019

Y1 - 2019

N2 - In this paper, we propose a fully automatic magnetic resonance image (MRI)-based computer aided diagnosis (CAD) system which simultaneously performs both prostate segmentation and prostate cancer diagnosis. The system utilizes a deep-learning approach to extract high-level features from raw T2-weighted MR volumes. Features are then remapped to the original input to assign a predicted label to each pixel. In the same context, we propose a 2.5D approach which exploits 3D spatial information without a compromise in computational cost. The system is evaluated on a public dataset. Preliminary results demonstrate that our approach outperforms current state-of-the-art in both prostate segmentation and cancer diagnosis.

AB - In this paper, we propose a fully automatic magnetic resonance image (MRI)-based computer aided diagnosis (CAD) system which simultaneously performs both prostate segmentation and prostate cancer diagnosis. The system utilizes a deep-learning approach to extract high-level features from raw T2-weighted MR volumes. Features are then remapped to the original input to assign a predicted label to each pixel. In the same context, we propose a 2.5D approach which exploits 3D spatial information without a compromise in computational cost. The system is evaluated on a public dataset. Preliminary results demonstrate that our approach outperforms current state-of-the-art in both prostate segmentation and cancer diagnosis.

UR - https://www.scopus.com/pages/publications/85061735791

U2 - 10.1007/978-3-030-11018-5_66

DO - 10.1007/978-3-030-11018-5_66

M3 - Conference contribution

AN - SCOPUS:85061735791

SN - 9783030110178

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 734

EP - 739

BT - Computer Vision – ECCV 2018 Workshops, Proceedings

A2 - Leal-Taixé, Laura

A2 - Roth, Stefan

PB - Springer Verlag

T2 - 15th European Conference on Computer Vision, ECCV 2018

Y2 - 8 September 2018 through 14 September 2018

ER -

Alkadi R, El-Baz A, Taher F, Werghi N. A 2.5D deep learning-based approach for prostate cancer detection on T2-weighted magnetic resonance imaging. In Leal-Taixé L, Roth S, editors, Computer Vision – ECCV 2018 Workshops, Proceedings. Springer Verlag. 2019. p. 734-739. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-11018-5_66

A 2.5D deep learning-based approach for prostate cancer detection on T2-weighted magnetic resonance imaging

Abstract

Publication series

Conference

UN SDGs

Access to Document

OpenUrl availability

Other files and links

Fingerprint

Cite this