CoFi: A Fast Coarse-to-Fine Few-Shot Pipeline for Glomerular Basement Membrane Segmentation

• URL: http://arxiv.org/abs/2508.11469v1
• Date: Fri, 15 Aug 2025 13:34:24 GMT
• Title: CoFi: A Fast Coarse-to-Fine Few-Shot Pipeline for Glomerular Basement Membrane Segmentation
• Authors: Hongjin Fang, Daniel Reisenbüchler, Kenji Ikemura, Mert R. Sabuncu, Yihe Yang, Ruining Deng,
• Abstract summary: We introduce CoFi, a fast and efficient few-shot segmentation pipeline designed for GBM delineation in EM images.<n>CoFi achieves exceptional GBM segmentation performance, with a Dice coefficient of 74.54% and an inference speed of 1.9 FPS.<n>The pipeline's speed and annotation make it well-suited for research and hold strong potential for clinical applications in renal pathology.
• Score: 6.053555523487327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate segmentation of the glomerular basement membrane (GBM) in electron microscopy (EM) images is fundamental for quantifying membrane thickness and supporting the diagnosis of various kidney diseases. While supervised deep learning approaches achieve high segmentation accuracy, their reliance on extensive pixel-level annotation renders them impractical for clinical workflows. Few-shot learning can reduce this annotation burden but often struggles to capture the fine structural details necessary for GBM analysis. In this study, we introduce CoFi, a fast and efficient coarse-to-fine few-shot segmentation pipeline designed for GBM delineation in EM images. CoFi first trains a lightweight neural network using only three annotated images to produce an initial coarse segmentation mask. This mask is then automatically processed to generate high-quality point prompts with morphology-aware pruning, which are subsequently used to guide SAM in refining the segmentation. The proposed method achieved exceptional GBM segmentation performance, with a Dice coefficient of 74.54% and an inference speed of 1.9 FPS. We demonstrate that CoFi not only alleviates the annotation and computational burdens associated with conventional methods, but also achieves accurate and reliable segmentation results. The pipeline's speed and annotation efficiency make it well-suited for research and hold strong potential for clinical applications in renal pathology. The pipeline is publicly available at: https://github.com/ddrrnn123/CoFi.

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