# Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps

arXiv: https://arxiv.org/abs/2308.04526 · HTML: https://mlsanity.com/paper/2308.04526/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 81 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 19 of 19)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation](https://mlsanity.com/paper/1503.00848/) (2015)
- [Learning to predict crisp boundaries](https://mlsanity.com/paper/1807.10097/) (2018)
- [Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts](https://mlsanity.com/paper/1505.06973/) (2015)
- [Convolutional Oriented Boundaries](https://mlsanity.com/paper/1608.02755/) (2016)
- [Graph Neural Network for Cell Tracking in Microscopy Videos](https://mlsanity.com/paper/2202.04731/) (2022)
- [EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths](https://mlsanity.com/paper/2204.10713/) (2022)
- [Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks](https://mlsanity.com/paper/1806.02070/) (2018)
- [MPM: Joint Representation of Motion and Position Map for Cell Tracking](https://mlsanity.com/paper/2002.10749/) (2020)
- [Machine learning of hierarchical clustering to segment 2D and 3D images](https://mlsanity.com/paper/1303.6163/) (2013)
- [GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation](https://mlsanity.com/paper/1906.11713/) (2019)
- [Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation](https://mlsanity.com/paper/2007.15258/) (2020)
- [Moral Lineage Tracing](https://mlsanity.com/paper/1511.05512/) (2015)
- [Efficient Algorithms for Moral Lineage Tracing](https://mlsanity.com/paper/1702.04111/) (2017)
- [Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages](https://mlsanity.com/paper/2208.11467/) (2022)

## Built on by (most cited first, 7 of 7)

- [Trackastra: Transformer-based cell tracking for live-cell microscopy](https://mlsanity.com/paper/2405.15700/) (2024)
- [Deep Temporal Sequence Classification and Mathematical Modeling for Cell Tracking in Dense 3D Microscopy Videos of Bacterial Biofilms](https://mlsanity.com/paper/2406.19574/) (2024)
- [Cell as Point: One-Stage Framework for Efficient Cell Tracking](https://mlsanity.com/paper/2411.14833/) (2024)
- [Accurate Tracking of Arabidopsis Root Cortex Cell Nuclei in 3D Time-Lapse Microscopy Images Based on Genetic Algorithm](https://mlsanity.com/paper/2504.12676/) (2025)
- [acia-workflows: Automated Single-cell Imaging Analysis for Scalable and Deep Learning-based Live-cell Imaging Analysis Workflows](https://mlsanity.com/paper/2510.05886/) (2025)
- [Higher-Order Cell Tracking Transformer](https://mlsanity.com/paper/2607.11754/) (2026)
- [SAM+D: Parameter-Efficient Dimensional Lifting of SAM-Family Models via Depth-Routed LoRA and Depth Shifting](https://mlsanity.com/paper/2607.29033/) (2026)

## Abstract (arXiv)

In this work, we describe a method for large-scale 3D cell-tracking through a segmentation selection approach. The proposed method is effective at tracking cells across large microscopy datasets on two fronts: (i) It can solve problems containing millions of segmentation instances in terabyte-scale 3D+t datasets; (ii) It achieves competitive results with or without deep learning, which requires 3D annotated data, that is scarce in the fluorescence microscopy field. The proposed method computes cell tracks and segments using a hierarchy of segmentation hypotheses and selects disjoint segments by maximizing the overlap between adjacent frames. We show that this method achieves state-of-the-art results in 3D images from the cell tracking challenge and has a faster integer linear programming formulation. Moreover, our framework is flexible and supports segmentations from off-the-shelf cell segmentation models and can combine them into an ensemble that improves tracking. The code is available https://github.com/royerlab/ultrack.