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The spatial and quantitative parameters of macular holes are vital fordiagnosis, surgical choices, and post-op monitoring. Macular hole diagnosis andtreatment rely heavily on spatial and quantitative data, yet the scarcity ofsuch data has impeded the progress of deep learning techniques for effectivesegmentation and real-time 3D reconstruction. To address this challenge, weassembled the world's largest macular hole dataset, Retinal OCTfor Macular HoleEnhancement (ROME-3914), and a Comprehensive Archive for Retinal Segmentation(CARS-30k), both expertly annotated. In addition, we developed an innovative 3Dsegmentation network, the Dual-Encoder FuGH Network (DEFN), which integratesthree innovative modules: Fourier Group Harmonics (FuGH), Simplified 3D SpatialAttention (S3DSA) and Harmonic Squeeze-and-Excitation Module (HSE). These threemodules 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 DefectInjection (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 retinalreconstruction and quantitative metrics, bringing revolutionary diagnostic andtherapeutic decision-making tools for ophthalmologists, and is expected tocompletely reshape the diagnosis and treatment patterns of difficult-to-treatmacular degeneration. The source code is publicly available at:https://github.com/IIPL-HangzhouDianUniversity/DEFN-Pytorch.

DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional  Macular Hole Reconstruction with Stochastic Retinal Defect Augmentation and  Dynamic Weight Composition