Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy

The accurate segmentation and tracking of cells in microscopy image sequences is an important task in biomedical research, e.g., for studying the development of tissues, organs or entire organisms. However, the segmentation of touching cells in images with a low signal-to-noise-ratio is still a challenging problem. In this paper, we present a method for the segmentation of touching cells in microscopy images. By using a novel representation of cell borders, inspired by distance maps, our method is capable to utilize not only touching cells but also close cells in the training process. Furthermore, this representation is notably robust to annotation errors and shows promising results for the segmentation of microscopy images containing in the training data underrepresented or not included cell types. For the prediction of the proposed neighbor distances, an adapted U-Net convolutional neural network (CNN) with two decoder paths is used. In addition, we adapt a graph-based cell tracking algorithm to evaluate our proposed method on the task of cell tracking. The adapted tracking algorithm includes a movement estimation in the cost function to re-link tracks with missing segmentation masks over a short sequence of frames. Our combined tracking by detection method has proven its potential in the IEEE ISBI 2020 Cell Tracking Challenge (http://celltrackingchallenge.net/) where we achieved as team KIT-Sch-GE multiple top three rankings including two top performances using a single segmentation model for the diverse data sets.Comment: 25 pages, 14 figures, methods of the team KIT-Sch-GE for the IEEE ISBI 2020 Cell Tracking Challeng

End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks

In this work we present a novel end-to-end framework for tracking and classifying a robot's surroundings in complex, dynamic and only partially observable real-world environments. The approach deploys a recurrent neural network to filter an input stream of raw laser measurements in order to directly infer object locations, along with their identity in both visible and occluded areas. To achieve this we first train the network using unsupervised Deep Tracking, a recently proposed theoretical framework for end-to-end space occupancy prediction. We show that by learning to track on a large amount of unsupervised data, the network creates a rich internal representation of its environment which we in turn exploit through the principle of inductive transfer of knowledge to perform the task of it's semantic classification. As a result, we show that only a small amount of labelled data suffices to steer the network towards mastering this additional task. Furthermore we propose a novel recurrent neural network architecture specifically tailored to tracking and semantic classification in real-world robotics applications. We demonstrate the tracking and classification performance of the method on real-world data collected at a busy road junction. Our evaluation shows that the proposed end-to-end framework compares favourably to a state-of-the-art, model-free tracking solution and that it outperforms a conventional one-shot training scheme for semantic classification

Inferring diffusion in single live cells at the single molecule level

The movement of molecules inside living cells is a fundamental feature of biological processes. The ability to both observe and analyse the details of molecular diffusion in vivo at the single molecule and single cell level can add significant insight into understanding molecular architectures of diffusing molecules and the nanoscale environment in which the molecules diffuse. The tool of choice for monitoring dynamic molecular localization in live cells is fluorescence microscopy, especially so combining total internal reflection fluorescence (TIRF) with the use of fluorescent protein (FP) reporters in offering exceptional imaging contrast for dynamic processes in the cell membrane under relatively physiological conditions compared to competing single molecule techniques. There exist several different complex modes of diffusion, and discriminating these from each other is challenging at the molecular level due to underlying stochastic behaviour. Analysis is traditionally performed using mean square displacements of tracked particles, however, this generally requires more data points than is typical for single FP tracks due to photophysical instability. Presented here is a novel approach allowing robust Bayesian ranking of diffusion processes (BARD) to discriminate multiple complex modes probabilistically. It is a computational approach which biologists can use to understand single molecule features in live cells.Comment: combined ms (1-37 pages, 8 figures) and SI (38-55, 3 figures

M-FISH analysis shows that complex chromosome aberrations induced by α-particle tracks are cumulative products of localised rearrangements

Complex chromosome aberrations are characteristically induced after exposure to low doses of densely ionising radiation, but little is understood about their formation. To address this, we irradiated human peripheral blood lymphocytes (PBL) in vitro with 0.5 Gy densely ionising α-particles (mean of 1 α-particle/cell) and analysed the chromosome aberrations produced using 24-colour M-FISH. Our data suggest that complex formation is a consequence of direct nuclear α-particle traversal and show that the likely product of illegitimate repair of damage from a single α-particle is a single complex exchange. From an assessment of the ‘cycle structure’ of each complex exchange we predict α-particle-induced damage to be repaired at specific localised sites, and complexes to be formed as cumulative products of this repair

Motion patterns of subviral particles: Digital tracking, image data processing and analysis

At the Institute of Virology, Philipps-University, Marburg, Germany, currently research on the understanding of the transport mechanisms of Ebola- and Marburgvirus nucleocapsids is carried out. This research demands a profound knowledge about the various motion characteristics of the nucleocapids. The analysis of large amounts of samples by conventional manual evaluation is a laborious task and does not always lead to reproducible and comparable results. In a cooperation between the Institute of Virology, Marburg, and the Institute for Biomedical Engineering, University of Applied Sciences, Giessen, Germany, algorithms are developed and programmed that enable an automatic tracking of subviral particles in fluorescence microscopic image sequences. The algorithms form an interface between the biologic and the algorithmic domain. Furthermore, methods to automatically parameterize and classify subviral particle motions are created. Geometric and mathematical approaches, like curvature-, fractal dimension- and mean squared displacement-determination are applied. Statistical methods are used to compare the measured subviral particle motion parameters between different biological samples. In this thesis, the biological, mathematical and algorithmic basics are described and the state of the art methods of other research groups are presented and compared. The algorithms to track, parameterize, classify and statistically analyze subviral particle tracks are presented in the Methods section. All methods are evaluated with simulated data and/or compared to data validated by a virologist. The methods are applied to a set of real fluorescence microscopic image sequences of Marburgvirus infected live-cells. The Results chapter shows that subviral particle motion can be successfully analyzed using the presented tracking and analysis methods. Furthermore, differences between the subviral particle motions in the analyzed groups could be detected. However, further optimization with manually evaluated data can improve the results. The methods developed in this project enhance the knowledge about nucleocapsid transport and may be valuable for the development of effective antiviral agents to cure Ebola- and Marburgvirus diseases. The thesis concludes with a chapter Discussion and Conclusions

Enhanced diffusion due to active swimmers at a solid surface

We consider two systems of active swimmers moving close to a solid surface, one being a living population of wild-type \textit{E. coli} and the other being an assembly of self-propelled Au-Pt rods. In both situations, we have identified two different types of motion at the surface and evaluated the fraction of the population that displayed ballistic trajectories (active swimmers) with respect to those showing random-like behavior. We studied the effect of this complex swimming activity on the diffusivity of passive tracers also present at the surface. We found that the tracer diffusivity is enhanced with respect to standard Brownian motion and increases linearly with the activity of the fluid, defined as the product of the fraction of active swimmers and their mean velocity. This result can be understood in terms of series of elementary encounters between the active swimmers and the tracers.Comment: 4 pages, 2 figures in color, Physical Review Letters (in production