Abstract
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 \(1.2\times 4\times 4\,\upmu \)m 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 \(30 \times 1000 \times 1000\) voxel blocks (\(1.2\times 4\times 4\,\upmu \)m) of densely annotated microtubules in the CREMI data set (https://github.com/nilsec/micron).
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Notes
- 1.
Resolution is around \(4\times 4 \times 40\) nm for ssTEM, and \(8\times 8 \times 8\) nm for FIB-SEM [20].
- 2.
MICCAI Challenge on Circuit Reconstruction in EM Images, https://cremi.org.
- 3.
We use our ILP formulation, which was necessary to process larger volumes.
- 4.
Orientation estimate used in [4] (direct communication with authors).
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Acknowledgements
We thank Tri Nguyen and Caroline Malin-Mayor for code contribution; Arlo Sheridan for helpful discussions and Albert Cardona for his contagious enthusiasm and support. This work was supported by Howard Hughes Medical Institute and Swiss National Science Foundation (SNF grant 205321L 160133).
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Eckstein, N., Buhmann, J., Cook, M., Funke, J. (2020). Microtubule Tracking in Electron Microscopy Volumes. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_10
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