Abstract
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.
Abstract (translated)
在计算显微镜学中，检测纤细且重叠的结构仍然是一个挑战。尽管最近基于坐标的检测方法有所改进，但它们通常生成的质量不如基于像素的方法产生的样条曲线（spline）。我们引入了一种无需训练的可微渲染方法来优化样条曲线，从而实现高可靠性和亚像素精度。我们的方法提升了样条质量、增强了对分布变化的鲁棒性，并缩小了合成数据与真实世界数据之间的差距。该方法完全无监督，可以作为流行的主动轮廓模型中用于样条曲线细化部分的直接替代方案。在秀丽隐杆线虫（C. elegans）中的评估表明，我们的方法结合了基于坐标和基于像素的方法的优点，而秀丽隐杆线虫是药物发现和生物医学研究中的常用模式生物。
URL
https://arxiv.org/abs/2503.14525
