Abstract
AIM: To construct an intelligent segmentation scheme for precise localization of central serous chorioretinopathy (CSC) leakage points, thereby enabling ophthalmologists to deliver accurate laser treatment without navigational laser equipment. METHODS: A dataset with dual labels (point-level and pixel-level) was first established based on fundus fluorescein angiography (FFA) images of CSC and subsequently divided into training (102 images), validation (40 images), and test (40 images) datasets. An intelligent segmentation method was then developed, based on the You Only Look Once version 8 Pose Estimation (YOLOv8-Pose) model and segment anything model (SAM), to segment CSC leakage points. Next, the YOLOv8-Pose model was trained for 200 epochs, and the best-performing model was selected to form the optimal combination with SAM. Additionally, the classic five types of U-Net series models [i.e., U-Net, recurrent residual U-Net (R2U-Net), attention U-Net (AttU-Net), recurrent residual attention U-Net (R2AttU-Net), and nested U-Net (UNet++)] were initialized with three random seeds and trained for 200 epochs, resulting in a total of 15 baseline models for comparison. Finally, based on the metrics including Dice similarity coefficient (DICE), intersection over union (IoU), precision, recall, precision-recall (PR) curve, and receiver operating characteristic (ROC) curve, the proposed method was compared with baseline models through quantitative and qualitative experiments for leakage point segmentation, thereby demonstrating its effectiveness. RESULTS: With the increase of training epochs, the mAP50-95, Recall, and precision of the YOLOv8-Pose model showed a significant increase and tended to stabilize, and it achieved a preliminary localization success rate of 90% (i.e., 36 images) for CSC leakage points in 40 test images. Using manually expert-annotated pixel-level labels as the ground truth, the proposed method achieved outcomes with a DICE of 57.13%, an IoU of 45.31%, a precision of 45.91%, a recall of 93.57%, an area under the PR curve (AUC-PR) of 0.78 and an area under the ROC curve (AUC-ROC) of 0.97, which enables more accurate segmentation of CSC leakage points. CONCLUSION: By combining the precise localization capability of the YOLOv8-Pose model with the robust and flexible segmentation ability of SAM, the proposed method not only demonstrates the effectiveness of the YOLOv8-Pose model in detecting keypoint coordinates of CSC leakage points from the perspective of application innovation but also establishes a novel approach for accurate segmentation of CSC leakage points through the "detect-then-segment" strategy, thereby providing a potential auxiliary means for the automatic and precise real-time localization of leakage points during traditional laser photocoagulation for CSC.