Abstract
Calcium imaging is one of the most important tools in neurophysiology as it enables the observation of neuronal activity for hundreds of cells in parallel and at single-cell resolution. In order to use the data gained with calcium imaging, it is necessary to extract individual cells and their activity from the recordings. We present DISCo, a novel approach for the cell segmentation in calcium imaging videos. We use temporal information from the recordings in a computationally efficient way by computing correlations between pixels and combine it with shape-based information to identify active as well as non-active cells. We first learn to predict whether two pixels belong to the same cell; this information is summarized in an undirected, edge-weighted graph which we then partition. Evaluating our method on the Neurofinder public benchmark shows that DISCo outperforms all existing models trained on these datasets.
E. Kirschbaum—This work was done while E.K. was at 1.
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Notes
- 1.
- 2.
We used the U-Net implementation provided in Inferno 0.3.0 with depth five, see https://github.com/inferno-pytorch/inferno.
- 3.
Leaderboard of the Neurofinder challenge at http://neurofinder.codeneuro.org. Accessed: 2020-03-05. We do not discuss the submissions Mask R-CNN and human-label since we have no information on the used models and training procedures.
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Acknowledgements
We gratefully acknowledge partial financial support by DFG SFB 1134
Functional Ensembles
and DFG HA 4364/9-1.
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Kirschbaum, E., Bailoni, A., Hamprecht, F.A. (2020). DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imaging. 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_15
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