基于人在环标注的吲哚菁绿血管造影图像脉络膜血管分割
Choroidal Vessel Segmentation on Indocyanine Green Angiography Images via Human-in-the-Loop Labeling
AI总结：
本研究利用人在环（HITL）框架开发高精度脉络膜血管分割模型，通过四轮迭代大幅减少人工标注时间，并在55度视角和超广角ICGA图像上实现高分割精度，同时引入了多个脉络膜异常风险预测因子。
摘要：
人在环（HITL）策略近期被引入医学图像处理领域。吲哚菁绿血管造影（ICGA）是观察脉络膜血管系统及检测脉络膜视网膜疾病的成熟检查方法。然而，脉络膜血管网络的复杂性使得大规模手动分割ICGA图像极具挑战。因此，本研究旨在利用HITL框架，以有限的人力开发高精度的脉络膜血管分割模型。我们使用了多源ICGA数据集，包括55度视角和超广角ICGA（UWF-ICGA）图像进行模型开发。脉络膜血管网络首先由预训练的血管分割模型进行预分割，随后由两名眼科医生手动修改。基于分割结果，自动量化了脉络膜血管直径、密度、复杂度、扭曲度和分支角度。我们最终进行了四个HITL周期。前三期使用了150张55度视角ICGA图像（每期50张），最后一期使用了20张UWF-ICGA图像。每期手动校正一张预分割ICGA图像的平均时间从20分钟降至1分钟。在55度视角ICGA和UWF-ICGA图像上均实现了高分割精度。此外，多维脉络膜血管参数与多种脉络膜视网膜疾病显著相关。本研究不仅证明了HITL策略在减少人工标注的同时提升分割性能的可行性，还创新性地引入了多个脉络膜异常风险预测因子。
英文摘要：
Human-in-the-loop (HITL) strategy has been recently introduced into the field of medical image processing. Indocyanine green angiography (ICGA) stands as a well-established examination for visualizing choroidal vasculature and detecting chorioretinal diseases. However, the intricate nature of choroidal vascular networks makes large-scale manual segmentation of ICGA images challenging. Thus, the study aims to develop a high-precision choroidal vessel segmentation model with limited labor using HITL framework. We utilized a multi-source ICGA dataset, including 55 degree view and ultra-widefield ICGA (UWF-ICGA) images for model development. The choroidal vessel network was pre-segmented by a pre-trained vessel segmentation model, and then manually modified by two ophthalmologists. Choroidal vascular diameter, density, complexity, tortuosity, and branching angle were automatically quantified based on the segmentation. We finally conducted four cycles of HITL. One hundred and fifty 55 degree view ICGA images were used for the first three cycles (50 images per cycle), and twenty UWF-ICGA images for the last cycle. The average time needed to manually correct a pre-segmented ICGA image per cycle reduced from 20 minutes to 1 minute. High segmentation accuracy has been achieved on both 55 degree view ICGA and UWF-ICGA images. Additionally, the multi-dimensional choroidal vascular parameters were significantly associated with various chorioretinal diseases. Our study not only demonstrated the feasibility of the HITL strategy in improving segmentation performance with reduced manual labeling, but also innovatively introduced several risk predictors for choroidal abnormalities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
