Zhang, J., Hu, Y., Qi, X., Meng, T., Wang, L., Fu, H., Yang, M., & Liu, J. (2023). Polar Eyeball Shape Net for 3D Posterior Ocular Shape Representation. In (Editor), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (pp. 180–190). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43987-2_18
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.
License type:
Publisher Copyright
Funding Info:
Central Research Fund
This research / project is supported by the A*STAR - AME Programmatic Fund
Grant Reference no. : A20H4b0141
Description:
This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-43987-2_18