Retinal Vessel Segmentation with Skeletal Prior and Contrastive Loss

Yubo Tan, Shixuan Zhao, Kaifu Yang, Yongjie Li

Abstract:

In this work, a new network called SkelCon is proposed to deal with
these problemsby introducingskeletal prior and contrastive
loss. A skeleton fitting module is developed to preserve
the morphology of the vessels and improve the completeness
and continuity of thin vessels. A contrastive loss is
employed to enhance the discrimination between vessels
and background. In addition, a new data augmentation
method is proposed to enrich the training samples and
improve the robustness of the proposed model. Extensive
validations were performed on several popular datasets
(DRIVE, STARE, CHASE, and HRF), recently developed
datasets (UoA-DR, IOSTAR, and RC-SLO), and some challenging
clinical images (from RFMiD and JSIEC39 datasets).
In addition, some specially designedmetrics for vessel segmentation,
including connectivity,overlapping area, consistency
of vessel length, revised sensitivity, specificity, and
accuracy were used for quantitative evaluation. The experimental
results show that, the proposed model achieves
state-of-the-art performance and significantly outperforms
compared methods when extracting thin vessels in the
regions of lesions or optic disc.