用可微渲染无监督优化细长结构的曲线，精度超像素级。
Spline refinement with differentiable rendering
- 基于可微渲染的无监督优化，不依赖训练数据
- 在秀丽隐杆线虫图像上实现亚像素级曲线精度
- 兼容主流主动轮廓模型，适合生物显微图像分析
细长重叠结构的检测仍是计算显微中的难题。尽管基于坐标的最新方法提升了检测效果，但其生成的曲线精度仍不及基于像素的方法。本文提出一种无需训练的可微渲染方法用于曲线精修，兼具高可靠性与亚像素级精度。该方法显著提升曲线质量，增强对分布偏移的鲁棒性，并缩小了合成数据与真实世界数据之间的差距。作为完全无监督的方法，它可直接替代流行的主动轮廓模型进行曲线精修。在模式生物秀丽隐杆线虫图像上的评估表明，该方法融合了坐标与像素方法的优势。
原文摘要 · Abstract (English)
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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