  Microscopy imaging plays a vital role in understanding many biological
processes in development and disease. The recent advances in automation of
microscopes and development of methods and markers for live cell imaging has
led to rapid growth in the amount of image data being captured. To efficiently
and reliably extract useful insights from these captured sequences, automated
cell tracking is essential. This is a challenging problem due to large
variation in the appearance and shapes of cells depending on many factors
including imaging methodology, biological characteristics of cells, cell matrix
composition, labeling methodology, etc. Often cell tracking methods require a
sequence-specific segmentation method and manual tuning of many tracking
parameters, which limits their applicability to sequences other than those they
are designed for. In this paper, we propose 1) a deep learning based cell
proposal method, which proposes candidates for cells along with their scores,
and 2) a cell tracking method, which links proposals in adjacent frames in a
graphical model using edges representing different cellular events and poses
joint cell detection and tracking as the selection of a subset of cell and edge
proposals. Our method is completely automated and given enough training data
can be applied to a wide variety of microscopy sequences. We evaluate our
method on multiple fluorescence and phase contrast microscopy sequences
containing cells of various shapes and appearances from ISBI cell tracking
challenge, and show that our method outperforms existing cell tracking methods.
  Code is available at: https://github.com/SaadUllahAkram/CellTracker
