I2PN: Improved Image Projection Network for OCTA image segmentation

Optical coherence tomography (OCT) is one of the most significant advances in medical images, and OCT segmentation is an important task in medical-assisted diagnostics. However, existing 3D processing methods require large computational resources. To alleviate this problem, we present an improved image projection network (I2PN), which is an end-to-end OCT image segmentation model. Our key insight is to build a simple projection module (SPM) that uses several group convolutions to conduct effective feature extraction and dimension reduction concurrently. By stacking multiple SPMs, the proposed network can process 3D OCTA data into a high-level semantic feature map. Afterward, the Down-Up modules (DUM) that contain down-sampling and up-sampling are applied to align the SPM features to the OCT image segmentation target. Finally, with a category-dependent segmentation head (CDS), which can balance region selection among hierarchical feature maps, the I2PN model can handle the OCTA image segmentation tasks simultaneously, such as retinal vessel and FAZ segmentation. I2PN provides a succinct idea for the quantification of retinal indicators. Extensive experiments on the OCTA-500 dataset validate the effectiveness of the proposed method I2PN.