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

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

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

11 Dyfyniadau(SciVal)
104 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

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.

Iaith wreiddiolSaesneg
Rhif yr erthygl83
CyfnodolynJournal of Imaging
Cyfrol6
Rhif cyhoeddi9
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 24 Awst 2020

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