Abstract
The shape of the posterior eyeball is a crucial factor in many clinical applications, such as myopia prevention, surgical planning, and disease screening. However, current shape representations are limited by their low resolution or small field of view, providing insufficient information for surgeons to make accurate decisions. This paper proposes a novel task of reconstructing complete 3D posterior shapes based on small-FOV OCT images and introduces a novel Posterior Eyeball Shape Network (PESNet) to accomplish this task. The proposed PESNet is designed with dual branches that incorporate anatomical information of the eyeball as guidance. To capture more detailed information, we introduce a Polar Voxelization Block (PVB) that transfers sparse input point clouds to a dense representation. Furthermore, we propose a Radius-wise Fusion Block (RFB) that fuses correlative hierarchical features from the two branches. Our qualitative results indicate that PESNet provides a well-represented complete posterior eyeball shape with a chamfer distance of 9.52, SSIM of 0.78, and Density of 0.013 on the self-made posterior ocular shape dataset. We also demonstrate the effectiveness of our model by testing it on patients’ data. Overall, our proposed PESNet offers a significant improvement over existing methods in accurately reconstructing the complete 3D posterior eyeball shape. This achievement has important implications for clinical applications.
J. Zhang and Y. Hu—Co-first authors.
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Acknowledgment
This work was supported in part by General Program of National Natural Science Foundation of China (82272086 and 82102189), Guangdong Basic and Applied Basic Research Foundation (2021A1515012195), Shenzhen Stable Support Plan Program (20220815111736001 and 20200925174052004), and Agency for Science, Technology and Research (A*STAR) Advanced Manufacturing and Engineering (AME) Programmatic Fund (A20H4b0141) and Central Research Fund (CRF).
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Zhang, J. et al. (2023). Polar Eyeball Shape Net for 3D Posterior Ocular Shape Representation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_18
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