DPDT: semi-supervised choroidal vessel segmentation via deformation perception and dynamic teacher selection

Segmentation of choroidal vessels is crucial for diagnosing various fundus diseases but poses a significant challenge. The complex and diverse morphology of choroidal vessels makes obtaining pixel-wise annotations extremely difficult. In this work, we introduce a labeling strategy that combines full annotation and sparse annotation to preserves the continuity features of the vessels while significantly reducing the annotation workload. To efficiently learn from both type of annotations, we propose a semi-supervised segmentation framework based on dual networks collaborative learning. In this framework, we propose an Uncertainty-Guided Dynamic Teacher Selection strategy (UGDTS) that dynamically selects the teacher network to generate pseudo-labels and supervises the other network under the guidance of the uncertainty map. Additionally, to reduce the cognitive bias of networks when using sparse annotations, we design a Deformation Perception Consistency (DPC) which enforces a weak-to-strong consistency constraint on the predictions before and after choroidal structural deformations. The quantitative and qualitative experimental results across various label proportions demonstrate the effectiveness and superiority of the proposed framework over other state-of-the-art methods.