Spline refinement with differentiable rendering Article Swipe

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Frans Zdyb , Albert Alonso , Julius B. Kirkegaard ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2503.14525

Detecting slender, overlapping structures remains a challenge in computational microscopy. While recent coordinate-based approaches improve detection, they often produce less accurate splines than pixel-based methods. We introduce a training-free differentiable rendering approach to spline refinement, achieving both high reliability and sub-pixel accuracy. Our method improves spline quality, enhances robustness to distribution shifts, and shrinks the gap between synthetic and real-world data. Being fully unsupervised, the method is a drop-in replacement for the popular active contour model for spline refinement. Evaluated on C. elegans nematodes, a popular model organism for drug discovery and biomedical research, we demonstrate that our approach combines the strengths of both coordinate- and pixel-based methods.

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Type: preprint
Language: en
Landing Page: https://doi.org/10.48550/arxiv.2503.14525
OA Status: green
OpenAlex ID: https://openalex.org/W4414900848

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DOI: https://doi.org/10.48550/arxiv.2503.14525

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Title: Spline refinement with differentiable rendering

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-03-15

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Authors: Frans Zdyb, Albert Alonso, Julius B. Kirkegaard

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Landing page: https://doi.org/10.48550/arxiv.2503.14525

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OA status: green

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OA URL: https://doi.org/10.48550/arxiv.2503.14525

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Cited by: 0

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