Improving prostate whole gland segmentation in t2-weighted MRI with synthetically generated data
This work addresses data scarcity in medical imaging for prostate cancer diagnosis, though it is incremental as it builds on existing GAN methods.
The paper tackled the problem of limited annotated data for prostate whole gland segmentation in T2-weighted MRI by using a GAN-based pipeline to generate synthetic images and masks, which improved segmentation quality compared to standard augmentation techniques.
Whole gland (WG) segmentation of the prostate plays a crucial role in detection, staging and treatment planning of prostate cancer (PCa). Despite promise shown by deep learning (DL) methods, they rely on the availability of a considerable amount of annotated data. Augmentation techniques such as translation and rotation of images present an alternative to increase data availability. Nevertheless, the amount of information provided by the transformed data is limited due to the correlation between the generated data and the original. Based on the recent success of generative adversarial networks (GAN) in producing synthetic images for other domains as well as in the medical domain, we present a pipeline to generate WG segmentation masks and synthesize T2-weighted MRI of the prostate based on a publicly available multi-center dataset. Following, we use the generated data as a form of data augmentation. Results show an improvement in the quality of the WG segmentation when compared to standard augmentation techniques.