At present, laser surgery is one of the effective ways to treat the chronic central serous chorioretinopathy (CSCR), in which the location of the leakage area is of great importance. In order to alleviate the pressure on ophthalmologists to manually label the biomarkers as well as elevate the biomarker segmentation quality, a semiautomatic biomarker segmentation method is proposed in this paper, aiming to facilitate the accurate and rapid acquisition of biomarker location information. Firstly, the multimodal fundus images are introduced into the biomarker segmentation task, which can effectively weaken the interference of highlighted vessels in the angiography images to the location of biomarkers. Secondly, a semiautomatic localization technique is adopted to reduce the search range of biomarkers, thus enabling the improvement of segmentation efficiency. On the basis of the above, the low-rank and sparse decomposition (LRSD) theory is introduced to construct the baseline segmentation scheme for segmentation of the CSCR biomarkers. Moreover, a joint segmentation framework consisting of the above method and region growing (RG) method is further designed to improve the performance of the baseline scheme. On the one hand, the LRSD is applied to offer the initial location information of biomarkers for the RG method, so as to ensure that the RG method can capture effective biomarkers. On the other hand, the biomarkers obtained by RG are fused with those gained by LRSD to make up for the defect of undersegmentation of the baseline scheme. Finally, the quantitative and qualitative ablation experiments have been carried out to demonstrate that the joint segmentation framework performs well than the baseline scheme in most cases, especially in the sensitivity and F1-score indicators, which not only confirms the effectiveness of the framework in the CSCR biomarker segmentation scene but also implies its potential application value in CSCR laser surgery.

中文翻译：

---

基于多模态眼底图像的慢性中央浆液性脉络膜视网膜病变生物标志物分割研究

目前，激光手术是治疗慢性中心性浆液性脉络膜视网膜病变（CSCR）的有效方法之一，其中渗漏区的位置非常重要。为了减轻眼科医生人工标注生物标志物的压力，提高生物标志物分割质量，本文提出了一种半自动生物标志物分割方法，旨在促进生物标志物位置信息的准确、快速获取。首先，将多模态眼底图像引入生物标志物分割任务，可以有效减弱血管造影图像中突出显示的血管对生物标志物位置的干扰。其次，采用半自动定位技术来缩小生物标志物的搜索范围，从而提高分割效率。在此基础上，引入低秩稀疏分解（LRSD）理论，构建CSCR生物标志物分割的基线分割方案。此外，进一步设计了由上述方法和区域增长（RG）方法组成的联合分割框架，以提高基线方案的性能。一方面，LRSD用于为RG方法提供生物标志物的初始位置信息，以确保RG方法能够捕获有效的生物标志物。另一方面，将RG获得的生物标志物与LRSD获得的生物标志物进行融合，以弥补基线方案分割不足的缺陷。最后，