Name
Title | ||
|---|---|---|
Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation. |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/jimaging6090083 | JOURNAL OF IMAGING |
Keywords | DocType | Volume |
prostate MRI,computer aided diagnosis,segmentation,detection,generative adversarial network | Journal | 6 |
Issue | ISSN | Citations |
9 | 2313-433X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ufuk Cem Birbiri | 1 | 0 | 0.34 |
| Azam Hamidinekoo | 2 | 19 | 3.48 |
| Amélie Grall | 3 | 0 | 0.34 |
| Paul Malcolm | 4 | 3 | 3.38 |
| Reyer Zwiggelaar | 5 | 711 | 103.74 |