# Fast spline detection in high density microscopy data

arXiv: https://arxiv.org/abs/2301.04460 · HTML: https://mlsanity.com/paper/2301.04460/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 108 references that are in the map.
- First posted on arXiv 11 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection](https://mlsanity.com/paper/1912.03538/) (2019)
- [Measuring behavior across scales](https://mlsanity.com/paper/1712.05784/) (2017)
- [DeepSpline: Data-Driven Reconstruction of Parametric Curves and Surfaces](https://mlsanity.com/paper/1901.03781/) (2019)
- [Deep Learning Parametrization for B-Spline Curve Approximation](https://mlsanity.com/paper/1807.08304/) (2018)
- [Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation](https://mlsanity.com/paper/1606.06127/) (2016)

## Built on by (most cited first, 4 of 4)

- [Spline refinement with differentiable rendering](https://mlsanity.com/paper/2503.14525/) (2025)
- [rNCA: Self-Repairing Segmentation Masks](https://mlsanity.com/paper/2512.13397/) (2025)
- [Extremal Contours: Gradient-driven contours for compact visual attribution](https://mlsanity.com/paper/2511.01411/) (2025)
- [CryoTracer: A Synthetic-to-Real System for Filament Instance Tracing in Cryo-EM](https://mlsanity.com/paper/2610.18696/) (2026)

## Abstract (arXiv)

Computer-aided analysis of biological microscopy data has seen a massive improvement with the utilization of general-purpose deep learning techniques. Yet, in microscopy studies of multi-organism systems, the problem of collision and overlap remains challenging. This is particularly true for systems composed of slender bodies such as crawling nematodes, swimming spermatozoa, or the beating of eukaryotic or prokaryotic flagella. Here, we develop a novel end-to-end deep learning approach to extract precise shape trajectories of generally motile and overlapping splines. Our method works in low resolution settings where feature keypoints are hard to define and detect. Detection is fast and we demonstrate the ability to track thousands of overlapping organisms simultaneously. While our approach is agnostic to area of application, we present it in the setting of and exemplify its usability on dense experiments of crawling Caenorhabditis elegans. The model training is achieved purely on synthetic data, utilizing a physics-based model for nematode motility, and we demonstrate the model's ability to generalize from simulations to experimental videos.