Polar Subarea-Aware Fusion Net for Posterior Eyeball Shape Reconstruction

High-fidelity reconstruction of the Posterior Eyeball Shape (PES) is crucial for early diagnosis and timely intervention of sight-threatening diseases such as high myopia, diabetic retinopathy, and glaucoma. However, existing magnetic resonance imaging (MRI)- and optical coherence tomography (OCT)-based methods either provide only coarse scleral geometry or suffer from suboptimal PES representations due to limited field of view (FOV) and detail loss, hindering accurate assessment of intact retinal pigment epithelium (RPE) abnormalities. In this study, we propose the Polar Subarea-Aware Fusion Net (PSAFNet), a novel end-to-end framework that reconstructs complete and high-fidelity PES directly from a single local OCT scan, even under clinically common settings with only 6.25% FOV. To avoid information loss, we reformulate PES reconstruction as a 2D dense regression task and introduce the Ocular Shape Map (OSM), an innovative lossless 2D representation that encodes 3D coordinate attributes into corresponding image channels. PSAFNet then leverages three dedicated modules-Subarea Feature Embedding Module (SFEM), Channel- and Patch-wise Fusion Blocks (CFB/PFB), and Reassemble and Up-sample Module (RUM)-to enhance positional awareness, integrate local-global features, and achieve high-resolution OSM prediction. Furthermore, we construct two large-scale datasets, POSDiag and PESGen, comprising 794 ultra-widefield OCT scans from diverse health conditions and imaging devices, providing a comprehensive benchmark for PES reconstruction. Extensive experiments demonstrate that PSAFNet consistently outperforms existing methods (e.g., EMD=5.58, AAL=97.3%) and exhibits strong clinical relevance, validated by superior performance in downstream disease classification and ophthalmologist evaluations (Expert-Score=82.78%). The source code of the proposed PSAFNet is released at https://github.com/HKUZJ77/PSAFNet.