Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DISCo: Deep learning, Instance Segmentation, and Correlations for cell
  segmentation in calcium imaging   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 grid
graph which we then partition. In so doing, we approximately solve the NP-hard
correlation clustering problem with a recently proposed greedy algorithm.
Evaluating our method on the Neurofinder public benchmark shows that DISCo
outperforms all existing models trained on these datasets.

8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci