# Microtubule Tracking in Electron Microscopy Volumes

arXiv: https://arxiv.org/abs/2009.08371 · HTML: https://mlsanity.com/paper/2009.08371/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Organelle-specific segmentation, spatial analysis, and visualization of volume electron microscopy datasets](https://mlsanity.com/paper/2303.03876/) (2023)
- [Catena: A Comprehensive Software Suite for Large-Scale Connectomics](https://mlsanity.com/paper/2609.21887/) (2026)

## Abstract (arXiv)

We present a method for microtubule tracking in electron microscopy volumes. Our method first identifies a sparse set of voxels that likely belong to microtubules. Similar to prior work, we then enumerate potential edges between these voxels, which we represent in a candidate graph. Tracks of microtubules are found by selecting nodes and edges in the candidate graph by solving a constrained optimization problem incorporating biological priors on microtubule structure. For this, we present a novel integer linear programming formulation, which results in speed-ups of three orders of magnitude and an increase of 53% in accuracy compared to prior art (evaluated on three 1.2 x 4 x 4μm volumes of Drosophila neural tissue). We also propose a scheme to solve the optimization problem in a block-wise fashion, which allows distributed tracking and is necessary to process very large electron microscopy volumes. Finally, we release a benchmark dataset for microtubule tracking, here used for training, testing and validation, consisting of eight 30 x 1000 x 1000 voxel blocks (1.2 x 4 x 4μm) of densely annotated microtubules in the CREMI data set (https://github.com/nilsec/micron).