With the continuous advancements in microscopy techniques such as improved image quality,
faster acquisition and reduced photo-toxicity, the amount of data recorded in the life sciences
is rapidly growing. Clearly, the size of the data renders manual analysis intractable, calling
for automated cell tracking methods. Cell tracking – in contrast to other tracking scenarios
– exhibits several difficulties: low signal to noise ratio in the images, high cell density and
sometimes cell clusters, radical morphology changes, but most importantly cells divide – which
is often the focus of the experiment. These peculiarities have been targeted by tracking-byassignment
methods that first extract a set of detection hypotheses and then track those over
time. Improving the general quality of these cell tracking methods is difficult, because every cell
type, surrounding medium, and microscopy setting leads to recordings with specific properties
and problems. This unfortunately implies that automated approaches will not become perfect
any time soon but manual proof reading by experts will remain necessary for the time being.
In this thesis we focus on two different aspects, firstly on scaling previous and developing new
solvers to deal with longer videos and more cells, and secondly on developing a specialized
pipeline for detecting and tracking tuberculosis bacteria.
The most powerful tracking-by-assignment methods are formulated as probabilistic graphical
models and solved as integer linear programs. Because those integer linear programs are in
general NP-hard, increasing the problem size will lead to an explosion of computational cost.
We begin by reformulating one of these models in terms of a constrained network flow, and
show that it can be solved more efficiently. Building on the successful application of network
flow algorithms in the pedestrian tracking literature, we develop a heuristic to integrate constraints
– here for divisions – into such a network flow method. This allows us to obtain high
quality approximations to the tracking solution while providing a polynomial runtime guarantee.
Our experiments confirm this much better scaling behavior to larger problems. However, this
approach is single threaded and does not utilize available resources of multi-core machines yet.
To parallelize the tracking problem we present a simple yet effective way of splitting long videos
into intervals that can be tracked independently, followed by a sparse global stitching step that
resolves disagreements at the cuts. Going one step further, we propose a microservices based
software design for ilastik that allows to distribute all required computation for segmentation,
object feature extraction, object classification and tracking across the nodes of a cluster or in the
cloud.
Finally, we discuss the use case of detecting and tracking tuberculosis bacteria in more
detail, because no satisfying automated method to this important problem existed before. One
peculiarity of these elongated cells is that they build dense clusters in which it is hard to outline individuals. To cope with that we employ a tracking-by-assignment model that allows competing
detection hypotheses and selects the best set of detections while considering the temporal context
during tracking. To obtain these hypotheses, we develop a novel algorithm that finds diverseM-
best solutions of tree-shaped graphical models by dynamic programming. First experiments
with the pipeline indicate that it can greatly reduce the required amount of human intervention
for analyzing tuberculosis treatment
