DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional
  Macular Hole Reconstruction with Stochastic Retinal Defect Augmentation and
  Dynamic Weight Composition
Xingru Huang Yihao Guo Jian Huang Zhi Li Tianyun Zhang Kunyan Cai Gaopeng Huang Wenhao Chen Zhaoyang Xu Liangqiong Qu Ji Hu Tinyu Wang Shaowei Jiang Chenggang Yan Yaoqi Sun Xin Ye Yaqi Wang
The spatial and quantitative parameters of macular holes are vital for
diagnosis, surgical choices, and post-op monitoring. Macular hole diagnosis and
treatment rely heavily on spatial and quantitative data, yet the scarcity of
such data has impeded the progress of deep learning techniques for effective
segmentation and real-time 3D reconstruction. To address this challenge, we
assembled the world's largest macular hole dataset, Retinal OCTfor Macular Hole
Enhancement (ROME-3914), and a Comprehensive Archive for Retinal Segmentation
(CARS-30k), both expertly annotated. In addition, we developed an innovative 3D
segmentation network, the Dual-Encoder FuGH Network (DEFN), which integrates
three innovative modules: Fourier Group Harmonics (FuGH), Simplified 3D Spatial
Attention (S3DSA) and Harmonic Squeeze-and-Excitation Module (HSE). These three
modules synergistically filter noise, reduce computational complexity,
emphasize detailed features, and enhance the network's representation ability.
We also proposed a novel data augmentation method, Stochastic Retinal Defect
Injection (SRDI), and a network optimization strategy DynamicWeightCompose
(DWC), to further improve the performance of DEFN. Compared with 13 baselines,
our DEFN shows the best performance. We also offer precise 3D retinal
reconstruction and quantitative metrics, bringing revolutionary diagnostic and
therapeutic decision-making tools for ophthalmologists, and is expected to
completely reshape the diagnosis and treatment patterns of difficult-to-treat
macular degeneration. The source code is publicly available at:
https://github.com/IIPL-HangzhouDianUniversity/DEFN-Pytorch.