Corneal Endothelial Cell Segmentation with Multiple Long-range Dependencies

Corneal endothelial cell segmentation is an important task in ophthalmology, but it is challenging due to variations in image characteristics across different datasets. Existing deep learning methods have limitations in capturing long-range dependencies that are critical for accurate segmentation. To address this issue, we propose a novel multiple long-range dependencies network (MLD-Net) that effectively incorporates different types of long-range dependency information to achieve robust segmentation across datasets. The network employs dilated convolutions and attention gates to capture spatial and layer-level dependencies, respectively. The entire network is densely connected, facilitating the sharing of long-range dependency information across multiple scales. We demonstrate the effectiveness of MLD-Net on four different corneal endothelium microscope image datasets: SREP, BiolmLab, Rodrep, and TM-EM3000. Our experimental results show that MLD-Net outperforms existing state-of-the-art methods, achieving robustness and high accuracy in corneal endothelial cell segmentation.