Segmentation Cycle-GAN Loss for Structure-Preserving Domain Adaptation of Medical Images

Unsupervised medical domain adaptation (DA) is an important field of study in medical image analysis, as domain shift is a very common issue for medical imaging. Unsupervised domain adaptation for the purpose of image segmentation on an unseen target domain has shown to be effective for brain MR scan problems. To improve the performance of unsupervised medical DA for segmentation, a Structure Preserving Cycle-GAN (SP Cycle-GAN) implementation was introduced. The SP Cycle-GAN was used to adapt STARE-domain retinal scans to the target DRIVE dataset domain, for the purpose of blood vessel segmentation via an Attention U-Net model. Pseudo-label generation on unlabelled source domain images was investigated to see if additional generated data could improve segmentation performance on the target domain. The implemented SP Cycle-GAN was shown to be effective for preserving structures in the source domain in translated target domain images. The SP Cycle-GAN however was not suited to the STARE and DRIVE datasets and did not outperform a simple baseline method of a trained segmentation model on the original source domain STARE data, which achieved a mean Dice Score (DSC) of 0.786 ± 0.015 on the DRIVE test dataset. The use of pseudo-labelled data to incorporate unlabelled source domain data for unsupervised DA was shown to have potential for improving performance, with a DSC of 0.718 ± 0.060 using pseudo-labelled data vs. 0.709 ± 0.063 when not using pseudo-labelled data. While the SP Cycle-GAN preserved blood vessel structure effectively, this resulted in another domain shift caused by the difference in overall shape of the scans. The introduced SP Cycle-GAN method shows promise for other DA datasets for medical image segmentation, in which small structures within an overall larger scan structure must be preserved.