Online Bayesian Approximation based Uncertainty Aware Model for Ophthalmic Image Segmentation.

The robust segmentation of different targets in multiple modality images is challenging due to factors such as low contrast, variations in target size and shape, and interference from diseases, which may lead to segmentation ambiguity. In addition, the assessment of the reliability of artificial intelligence is crucial for its clinical application. This paper proposes the Online Bayesian approximation based Uncertainty-aware Network (OBU-Net) for robust ophthalmic image segmentation.

Prior Anatomical Knowledge-guided GAN for ICL surgery postoperative prediction based on AS-OCT image.

Accurate prediction of postoperative vault, the distance between the implantable collamer lens (ICL) posterior surface and the crystalline lens anterior surface, is critical for the success of ICL surgery. Existing regression-based prediction methods fail to provide visual postoperative observations, which are essential for a comprehensive risk assessment. Anterior segment optical coherence tomography (AS-OCT) enables high-resolution visualization of anterior segment structures.

Structural Priors Guided Network for the Corneal Endothelial Cell Segmentation.

The segmentation of blurred cell boundaries in cornea endothelium microscope images is challenging, which affects the clinical parameter estimation accuracy. Existing deep learning methods only consider pixel-wise classification accuracy and lack of utilization of cell structure knowledge. Therefore, the segmentation of the blurred cell boundary is discontinuous.