@conference {65837,
	title = {Microtubule Tracking in Electron Microscopy Volumes},
	booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
	year = {2020},
	month = {2020 Sep 17},
	abstract = {<p>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 {\texttimes} 4 {\texttimes} 4{\textmu}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 x 1000 x 1000 voxel blocks (1 . 2 {\texttimes} 4 {\texttimes} 4{\textmu}m) of densely annotated microtubules in the CREMI data set (<a href="https://github.com/nilsec/micron" target="_blank">https://github.com/nilsec/micron</a>).</p>
},
	author = {Nils Eckstein and Julia Buhmann and Matthew Cook and Jan Funke}
}
