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


Birbiri U. C. , Hamidinekoo A., Grall A., Malcolm P., Zwiggelaar R.

JOURNAL OF IMAGING, vol.6, no.9, 2020 (Journal Indexed in ESCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 6 Issue: 9
  • Publication Date: 2020
  • Doi Number: 10.3390/jimaging6090083
  • Title of Journal : JOURNAL OF IMAGING
  • Keywords: prostate MRI, computer aided diagnosis, segmentation, detection, generative adversarial network

Abstract

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.