Image Projection Network: 3D to 2D Image Segmentation in OCTA Images

• Published in: IEEE transactions on medical imaging Vol. 39; no. 11; pp. 3343 - 3354
• Main Authors: Li, Mingchao, Chen, Yerui, Ji, Zexuan, Xie, Keren, Yuan, Songtao, Chen, Qiang, Li, Shuo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D to 2D; Angiography; Biomedical imaging; biomedical volumetric image segmentation; Blood vessels; End to end; Image processing; image projection network; Image segmentation; optical coherence tomography angiography; Projection; Retina; Retinal images; Retinal vessels; Semantics; Three-dimensional displays; Two dimensional displays
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2020.2992244

We present an image projection network (IPN), which is a novel end-to-end architecture and can achieve 3D-to-2D image segmentation in optical coherence tomography angiography (OCTA) images. Our key insight is to build a projection learning module (PLM) which uses a unidirectional pooling layer to conduct effective features selection and dimension reduction concurrently. By combining multiple PLMs, the proposed network can input 3D OCTA data, and output 2D segmentation results such as retinal vessel segmentation. It provides a new idea for the quantification of retinal indicators: without retinal layer segmentation and without projection maps. We tested the performance of our network for two crucial retinal image segmentation issues: retinal vessel (RV) segmentation and foveal avascular zone (FAZ) segmentation. The experimental results on 316 OCTA volumes demonstrate that the IPN is an effective implementation of 3D-to-2D segmentation networks, and the uses of multi-modality information and volumetric information make IPN perform better than the baseline methods.