Medical image segmentation of systemic lupus erythematosus related cerebral small vessel disease

Image segmentation is an important and difficult task in many medical applications. The segmentation results can be used to help doctors diagnose diseases, observe the lesion areas, and make surgical plans. Traditional medical image segmentation methods rely on experienced radiologists, which makes medical image segmentation time-consuming and costly. With the help of computer technology, the use of deep learning neural network for medical image segmentation has been applied in multiple disease lesions or organ segmentation tasks. Cerebral small vessel disease is a manifestation of systemic lupus erythematosus. In this paper, we apply Unet, Nested Unet, and Recurrent Residual Unet to segment the collected medical images of systemic lupus erythematosus patients. The results of Unet and Nested Unet is better than Recurrent Residual Unet. All of the three networks can segment parts of lesion areas, but small areas are missed. The results of our experiments showed the performance of the existing segmentation neural networks on our dataset still need to be further improved to meet the requirements of practical applications.