Retina-TransNet: A Gradient-Guided Few-Shot Retinal Vessel Segmentation Net

IEEE J Biomed Health Inform. 2023 Oct;27(10):4902-4913. doi: 10.1109/JBHI.2023.3298710. Epub 2023 Oct 5.


Due to the high labor cost of physicians, it is difficult to collect a rich amount of manually-labeled medical images for developing learning-based computer-aided diagnosis (CADx) systems or segmentation algorithms. To tackle this issue, we reshape the image segmentation task as an image-to-image (I2I) translation problem and propose a retinal vascular segmentation network, which can achieve good cross-domain generalizability even with a small amount of training data. We devise primarily two components to facilitate this I2I-based segmentation method. The first is the constraints provided by the proposed gradient-vector-flow (GVF) loss, and, the second is a two-stage Unet (2Unet) generator with a skip connection. This configuration makes 2Unet's first-stage play a role similar to conventional Unet, but forces 2Unet's second stage to learn to be a refinement module. Extensive experiments show that by re-casting retinal vessel segmentation as an image-to-image translation problem, our I2I translator-based segmentation subnetwork achieves better cross-domain generalizability than existing segmentation methods. Our model, trained on one dataset, e.g., DRIVE, can produce segmentation results stably on datasets of other domains, e.g., CHASE-DB1, STARE, HRF, and DIARETDB1, even in low-shot circumstances.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Diagnosis, Computer-Assisted
  • Fundus Oculi
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Retina* / diagnostic imaging
  • Retinal Vessels / diagnostic imaging