Polar Eyeball Shape Net for 3D Posterior Ocular Shape Representation

Zhang, Jiaqi1,2; Hu, Yan2; Qi, Xiaojuan1; Meng, Ting3; Wang, Lihui4; Fu, Huazhu5; Yang, Mingming3; Liu, Jiang2

10.1007/978-3-031-43987-2_18

2023

26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

0302-9743

1611-3349

978-3-031-43986-5

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2023, PT VI

14225

OCT 08-12, 2023

null,Vancouver,CANADA

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

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.

Posterior eyeball shape 3D reconstruction Polar transformation Anatomical prior guided

通讯

英语

EI ; CPCI-S

General Program of National Natural Science Foundation of China["82272086","82102189"] ; Guangdong Basic and Applied Basic Research Foundation[2021A1515012195] ; Shenzhen Stable Support Plan Program["20220815111736001","20200925174052004"] ; Agency for Science, Technology and Research (A*STAR) Advanced Manufacturing and Engineering (AME) Programmatic Fund[A20H4b0141]

Computer Science ; Radiology, Nuclear Medicine & Medical Imaging

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Radiology, Nuclear Medicine & Medical Imaging

WOS:001109635100018

Web of Science

1.The University of Hong Kong, Pokfulam, Hong Kong
2.Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
3.Department of Ophthalmology, Shenzhen People’s Hospital, Shenzhen, China
4.Institute of Semiconductors, Guangdong Academy of Sciences, Guangzhou, China
5.Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore, Singapore

Zhang, Jiaqi,Hu, Yan,Qi, Xiaojuan,et al. Polar Eyeball Shape Net for 3D Posterior Ocular Shape Representation[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2023.