An intelligent segmentation method for leakage points in central serous chorioretinopathy based on fluorescein angiography images.

aimTo 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.

methodsA 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 [

resultsWith 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% (

conclusionBy 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.