Resource Constrained U-Net for Extraction of Retinal Vascular Trees
arXiv:2407.04940v1h-index: 1
Originality Synthesis-oriented
AI Analysis
This work addresses the extraction of retinal vascular trees for medical imaging applications, but it is incremental as it builds on existing U-Net methods with minor modifications.
The paper tackled the problem of extracting retinal vascular trees from fundus photographs using a modified U-Net structure, achieving performance that only slightly underperforms state-of-the-art methods under constraints of limited compute resources and training data.
This paper demonstrates the efficacy of a modified U-Net structure for the extraction of vascular tree masks for human fundus photographs. On limited compute resources and training data, the proposed model only slightly underperforms when compared to state of the art methods.