🤖 AI Summary
This study addresses structural discontinuities in anisotropic microscopy images caused by insufficient axial sampling by proposing an end-to-end, GPU-accelerated segmentation framework that requires no dense 3D annotations. The method trains 3D U-Net or SwinUNETR models directly on raw anisotropic volumes, incorporating random rotation-based data augmentation and a Z-axis continuity loss to enhance inter-slice coherence. Accurate quantification of membrane thickness is achieved through Gaussian consensus fusion combined with a point spread function–corrected, GPU-accelerated ray-surface intersection algorithm. This work presents the first fully automated, image-restoration-free morphometric analysis of nanoscale glomerular basement membranes (GBM), attaining segmentation accuracy comparable to inter-expert agreement, substantially suppressing staircasing artifacts, improving reconstruction smoothness, and successfully detecting disease-associated GBM thickening.
📝 Abstract
Confocal microscopy of optically cleared and swelled tissue resolves complex biological structures in 3D, but such acquisitions are highly anisotropic: along the under-sampled axial direction the structure can appear discontinuous, hampering reconstruction and automated quantitative analysis. The usual remedy upsamples the axial dimension to an isotropic volume before training a segmentation model, which requires dense annotations in the upsampled space, a prohibitive labeling burden.
We present an end-to-end, GPU-accelerated framework that overcomes this without additional annotations. The model is trained on the native acquisition volume; random rotation of training patches leverages the well-resolved lateral plane to supply the missing axial information, and a z-axis continuity loss keeps neighboring slices consistent. We adapt both a convolutional (3D U-Net) and a transformer (SwinUNETR) backbone, aggregate overlapping patches by Gaussian consensus, and compute point-spread-function-corrected membrane thickness by ray-surface intersection on the GPU.
We apply the method to the glomerular basement membrane (GBM), a thin, highly convoluted part of the kidney's filtration barrier that grows more irregular in disease. Segmentation accuracy matches inter-expert agreement. Continuity-aware training improves reconstruction smoothness and suppresses a periodic terracing artifact at minimal accuracy cost. We quantify GBM thickness across the reconstructed 3D surface and capture disease-related thickening, enabling fully automated anisotropic 3D morphometry of biological structures without dense volumetric labels or image restoration.