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, cilt.6, 2020 (ESCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 6 Konu: 9
  • Basım Tarihi: 2020
  • Doi Numarası: 10.3390/jimaging6090083
  • Dergi Adı: JOURNAL OF IMAGING

Özet

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.