{"slug": "beyond-isotropic-assumptions-continuity-constrained-segmentation-and-gpu-for-gbm", "title": "Beyond Isotropic Assumptions: Continuity-Constrained Segmentation and GPU Morphometry for Nanoscale GBM Analysis", "summary": "Researchers introduced a GPU-accelerated framework for segmenting anisotropic 3D microscopy volumes without dense annotations, achieving accuracy matching inter-expert agreement. The method, applied to kidney glomerular basement membrane analysis, captures disease-related thickening and enables fully automated morphometry.", "body_md": "arXiv:2608.07575v1 Announce Type: new\nAbstract: 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.\nWe 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.\nWe 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.", "url": "https://wpnews.pro/news/beyond-isotropic-assumptions-continuity-constrained-segmentation-and-gpu-for-gbm", "canonical_source": "https://arxiv.org/abs/2608.07575", "published_at": "2026-08-11 04:00:00+00:00", "updated_at": "2026-08-11 04:24:48.323489+00:00", "lang": "en", "topics": ["computer-vision", "machine-learning"], "entities": ["arXiv", "SwinUNETR", "3D U-Net"], "alternates": {"html": "https://wpnews.pro/news/beyond-isotropic-assumptions-continuity-constrained-segmentation-and-gpu-for-gbm", "markdown": "https://wpnews.pro/news/beyond-isotropic-assumptions-continuity-constrained-segmentation-and-gpu-for-gbm.md", "text": "https://wpnews.pro/news/beyond-isotropic-assumptions-continuity-constrained-segmentation-and-gpu-for-gbm.txt", "jsonld": "https://wpnews.pro/news/beyond-isotropic-assumptions-continuity-constrained-segmentation-and-gpu-for-gbm.jsonld"}}