Investigating the performance of generative adversarial networks for prostate tissue detection and segmentation

Ufuk Cem Birbiri, Azam Hamidinekoo, Amélie Grall, Paul Malcolm, Reyer Zwiggelaar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (SciVal)
99 Downloads (Pure)

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.

Original languageEnglish
Article number83
JournalJournal of Imaging
Volume6
Issue number9
DOIs
Publication statusPublished - 24 Aug 2020

Keywords

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

Fingerprint

Dive into the research topics of 'Investigating the performance of generative adversarial networks for prostate tissue detection and segmentation'. Together they form a unique fingerprint.

Cite this