Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation

Date

2020-09-01

Author

Birbiri, Ufuk Cem
Hamidinekoo, Azam
Grall, Amelie
Malcolm, Paul
Zwiggelaar, Reyer

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The manual delineation of region of interest (RoI) in 3D magnetic resonance imaging (MRI) of the prostate is time-consuming and subjective. Correct identification of prostate tissue is helpful to define a precise RoI to be used in CAD systems in clinical practice during diagnostic imaging, radiotherapy and monitoring the progress of disease. Conditional GAN (cGAN), cycleGAN and U-Net models and their performances were studied for the detection and segmentation of prostate tissue in 3D multi-parametric MRI scans. These models were trained and evaluated on MRI data from 40 patients with biopsy-proven prostate cancer. Due to the limited amount of available training data, three augmentation schemes were proposed to artificially increase the training samples. These models were tested on a clinical dataset annotated for this study and on a public dataset (PROMISE12). The cGAN model outperformed the U-Net and cycleGAN predictions owing to the inclusion of paired image supervision. Based on our quantitative results, cGAN gained a Dice score of 0.78 and 0.75 on the private and the PROMISE12 public datasets, respectively.

Subject Keywords

Prostate MRI, Generative adversarial network, Segmentation, Computer aided diagnosis, Detection

URI

https://hdl.handle.net/11511/68122

Journal

JOURNAL OF IMAGING

DOI

https://doi.org/10.3390/jimaging6090083

Collections

Department of Computer Engineering, Article

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Citation Formats

U. C. Birbiri, A. Hamidinekoo, A. Grall, P. Malcolm, and R. Zwiggelaar, “Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation,” JOURNAL OF IMAGING, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/68122.