提出一种无需标注的血管连通性增强方法，提升多模态眼底图像血管分割精度。
Local-sensitive connectivity filter (ls-cf): A post-processing unsupervised improvement of the frangi, hessian and vesselness filters for multimodal vessel segmentation
- 基于局部容差机制，修复Frangi滤波器产生的血管断裂
- 在5个数据集上超越多数现有方法，尤其在OSIRIX上全面领先
- 适用于无监督场景，适合临床眼底影像自动分析应用
视网膜血管分析可用于评估眼部疾病风险。本文提出一种无监督多模态方法，改进Frangi滤波器响应，实现自动血管分割。所提局部敏感连通性滤波器（LS-CF）通过计算像素级血管连续性，并引入局部容差机制填补Frangi响应造成的血管断点。该方法与基线阈值化Frangi响应、朴素连通性滤波器及结合形态学闭运算的方法进行对比，并与文献中当前主流方法比较。在多个多模态数据集上表现优异：在OSIRIX血管造影数据集上，准确率全面超越现有方法；在IOSTAR数据集上优于5篇中的4篇；在DRIVE和STARE数据集上优于多数方法；在CHASE-DB数据集上优于10篇中的6篇，且优于所有现有无监督方法。
原文摘要 · Abstract (English)
A retinal vessel analysis is a procedure that can be used as an assessment of risks to the eye. This work proposes an unsupervised multimodal approach that improves the response of the Frangi filter, enabling automatic vessel segmentation. We propose a filter that computes pixel-level vessel continuity while introducing a local tolerance heuristic to fill in vessel discontinuities produced by the Frangi response. This proposal, called the local-sensitive connectivity filter (LS-CF), is compared against a naive connectivity filter to the baseline thresholded Frangi filter response and to the naive connectivity filter response in combination with the morphological closing and to the current approaches in the literature. The proposal was able to achieve competitive results in a variety of multimodal datasets. It was robust enough to outperform all the state-of-the-art approaches in the literature for the OSIRIX angiographic dataset in terms of accuracy and 4 out of 5 works in the case of the IOSTAR dataset while also outperforming several works in the case of the DRIVE and STARE datasets and 6 out of 10 in the CHASE-DB dataset. For the CHASE-DB, it also outperformed all the state-of-the-art unsupervised methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。