Tracking Tetrahymena Pyriformis Cells using Decision Trees
This addresses the challenge of cell tracking in biological imaging, but appears incremental as it applies existing machine learning techniques to a specific domain.
The paper tackled the problem of matching cells over time in cell tracking by recasting it as a classification problem using decision trees, resulting in a method that formulates and solves an assignment problem with a modified Hungarian algorithm.
Matching cells over time has long been the most difficult step in cell tracking. In this paper, we approach this problem by recasting it as a classification problem. We construct a feature set for each cell, and compute a feature difference vector between a cell in the current frame and a cell in a previous frame. Then we determine whether the two cells represent the same cell over time by training decision trees as our binary classifiers. With the output of decision trees, we are able to formulate an assignment problem for our cell association task and solve it using a modified version of the Hungarian algorithm.