Scale Selection and Machine Learning-based Cell Segmentation and Tracking in Time Lapse Microscopy.

Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation—including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6th edition of the Cell Tracking Challenge.

A probabilistic approach to joint cell tracking and segmentation in high-throughput microscopy videos.

Computational analysis of fluorescent timelapse microscopy images at the single-cell level is a powerful approach to study cellular changes that dictate important cell fate decisions. Core to this approach is the need to generate reliable cell segmentations and classifications necessary for accurate quantitative analysis. Deep learning-based convolutional neural networks (CNNs) have emerged as a promising solution to these challenges. However, current CNNs are prone to produce noisy cell segmentations and classifications, which is a significant barrier to constructing accurate single-cell lineages. To address this, we developed a novel algorithm called Single Cell Track (SC-Track), which employs a hierarchical probabilistic cache cascade model based on biological observations of cell division and movement dynamics. Our results show that SC-Track performs better than a panel of publicly available cell trackers on a diverse set of cell segmentation types. This cell-tracking performance was achieved without any parameter adjustments, making SC-Track an excellent generalized algorithm that can maintain robust cell-tracking performance in varying cell segmentation qualities, cell morphological appearances and imaging conditions. Furthermore, SC-Track is equipped with a cell class correction function to improve the accuracy of cell classifications in multiclass cell segmentation time series. These features together make SC-Track a robust cell-tracking algorithm that works well with noisy cell instance segmentation and classification predictions from CNNs to generate accurate single-cell lineages and classifications.

The swarming behavior of Pseudomonas aeruginosa is an important factor to the formation of bacteria biofilm which may cause infections in susceptible individuals. A key issue in studying the mechanism of P. aeruginosa swarming is to analyze the cell dynamics in time-lapse images. We propose a complete toolkit for producing quantitative results of cell dynamics analysis in time-lapse images of P. aeruginosa. The toolkit includes automatic cell segmentation and tracking, and optional tools for human interaction. Our proposed segmentation and tracking algorithms achieve considerable improvement over one of the best known open-source software for cell segmentation and tracking. A set of graphical interfaces is developed for human annotation, segmentation and tracking results inspection and correction, which are generic and can be combined with most other cell segmentation and tracking algorithms. Our toolkit is convenient for scientific researchers to use, even without knowledge of image processing, to perform cell dynamics analysis efficiently.

Cell tracking is a crucial component of many biomedical image analysis applications. Many available cell tracking systems assume high precision of the cell detection module. Therefore low performance in cell detection can heavily affect the tracking results. Unfortunately cell segmentation modules often have significant errors, especially in the case of phase-contrast imaging. In this paper we propose a tracking method that does not rely on perfect cell segmentation and can deal with uncertainties by exploiting temporal information and aggregating the results of many frames. Our tracking algorithm is fully automated and can handle common challenges of tracking such as cells entering/exiting the screen and mitosis events. To handle the latter, we modify the standard flow network and introduce the concept of a splitting node into it. Experiment results show that adding temporal information from the video microscopy improves the cell/mitosis detection and results in a better tracking system.

Robust circadian oscillations of the proteins PERIOD (PER) and TIMELESS (TIM) are hallmarks of a functional clock in the fruit fly Drosophila melanogaster. Early morning phosphorylation of PER by the kinase Doubletime (DBT) and subsequent PER turnover is an essential step in the functioning of the Drosophila circadian clock. Here using time-lapse fluorescence microscopy we study PER stability in the presence of DBT and its short, long, arrhythmic, and inactive mutants in S2 cells. We observe robust PER degradation in a DBT allele-specific manner. With the exception of doubletime-short (DBT(S)), all mutants produce differential PER degradation profiles that show direct correspondence with their respective Drosophila behavioral phenotypes. The kinetics of PER degradation with DBT(S) in cell culture resembles that with wild-type DBT and posits that, in flies DBT(S) likely does not modulate the clock by simply affecting PER degradation kinetics. For all the other tested DBT alleles, the study provides a simple model in which the changes in Drosophila behavioral rhythms can be explained solely by changes in the rate of PER degradation.

Migration and interactions of immune cells are routinely studied by time-lapse microscopy of in vitro migration and confrontation assays. To objectively quantify the dynamic behavior of cells, software tools for automated cell tracking can be applied. However, many existing tracking algorithms recognize only rather short fragments of a whole cell track and rely on cell staining to enhance cell segmentation. While our previously developed segmentation approach enables tracking of label-free cells, it still suffers from frequently recognizing only short track fragments. In this study, we identify sources of track fragmentation and provide solutions to obtain longer cell tracks. This is achieved by improving the detection of low-contrast cells and by optimizing the value of the gap size parameter, which defines the number of missing cell positions between track fragments that is accepted for still connecting them into one track. We find that the enhanced track recognition increases the average length of cell tracks up to 2.2-fold. Recognizing cell tracks as a whole will enable studying and quantifying more complex patterns of cell behavior, e.g. switches in migration mode or dependence of the phagocytosis efficiency on the number and type of preceding interactions. Such quantitative analyses will improve our understanding of how immune cells interact and function in health and disease.

Accurate cell segmentation is vital for the development of reliable microscopy image analysis methods. It is a very challenging problem due to low contrast, weak boundaries, and conjoined and overlapping cells; producing many ambiguous regions, which lower the performance of automated segmentation methods. Cell proposals provide an efficient way of exploiting both spatial and temporal context, which can be very helpful in many of these ambiguous regions. However, most proposal based microscopy image analysis methods rely on fairly simple proposal generation stage, limiting their performance. In this paper, we propose a convolutional neural network based method which provides cell segmentation proposals, which can be used for cell detection, segmentation and tracking. We evaluate our method on datasets from histology, fluorescence and phase contrast microscopy and show that it outperforms state of the art cell detection and segmentation methods.

We propose a novel weakly supervised method to improve the boundary of the 3D segmented nuclei utilizing an over-segmented image. This is motivated by the observation that current state-of-the-art deep learning methods do not result in accurate boundaries when the training data is weakly annotated. Towards this, a 3D U-Net is trained to get the centroid of the nuclei and integrated with a simple linear iterative clustering (SLIC) supervoxel algorithm that provides better adherence to cluster boundaries. To track these segmented nuclei, our algorithm utilizes the relative nuclei location depicting the processes of nuclei division and apoptosis. The proposed algorithmic pipeline achieves better segmentation performance compared to the state-of-the-art method in Cell Tracking Challenge (CTC) 2019 and comparable performance to state-of-the-art methods in IEEE ISBI CTC2020 while utilizing very few pixel-wise annotated data. Detailed experimental results are provided, and the source code is available on GitHub.

BackgroundHigh-throughput live-cell imaging is a powerful tool to study dynamic cellular processes in single cells but creates a bottleneck at the stage of data analysis, due to the large amount of data generated and limitations of analytical pipelines. Recent progress on deep learning dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and tools spanning the complete range of image analysis are still needed.ResultsWe present Cell-ACDC, an open-source user-friendly GUI-based framework written in Python, for segmentation, tracking and cell cycle annotations. We included state-of-the-art deep learning models for single-cell segmentation of mammalian and yeast cells alongside cell tracking methods and an intuitive, semi-automated workflow for cell cycle annotation of single cells. Using Cell-ACDC, we found that mTOR activity in hematopoietic stem cells is largely independent of cell volume. By contrast, smaller cells exhibit higher p38 activity, consistent with a role of p38 in regulation of cell size. Additionally, we show that, in S. cerevisiae, histone Htb1 concentrations decrease with replicative age.ConclusionsCell-ACDC provides a framework for the application of state-of-the-art deep learning models to the analysis of live cell imaging data without programming knowledge. Furthermore, it allows for visualization and correction of segmentation and tracking errors as well as annotation of cell cycle stages. We embedded several smart algorithms that make the correction and annotation process fast and intuitive. Finally, the open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation, tracking, and downstream image analysis.Source code: https://github.com/SchmollerLab/Cell_ACDC

This paper presents hardware and software procedures for automated cell tracking and migration modeling. A time-lapse microscopy system equipped with a computer controllable motorized stage was developed. The performance of this stage was improved by incorporating software algorithms for stage motion displacement compensation and auto focus. The microscope is suitable for in-vitro stem cell studies and allows for multiple cell culture image sequence acquisition. This enables comparative studies concerning rate of cell splits, average cell motion velocity, cell motion as a function of cell sample density and many more. Several cell segmentation procedures are described as well as a cell tracking algorithm. Statistical methods for describing cell migration patterns are presented. In particular, the Hidden Markov Model (HMM) was investigated. Results indicate that if the cell motion can be described as a non-stationary stochastic process, then the HMM can adequately model aspects of its dynamic behavior.

Cell segmentation is a crucial step for understanding cell mechanisms, behaviors and for analyzing them to improve health and drug discovery. In this work, we propose an automated system for cell segmentation in video-sequences by non-linear diffusion partial differential equations (PDE) modeling extended to the joint spatio-temporal domain and region-based levelset optimization. Our first step is intensity standardization by histogram transformation, to reduce the intensity variability across all the frames. After this step, moving regions are detected in each set of three consecutive frames by solving a non linear PDE in spatio-temporal domain, using the optimal spatial and temporal parameters. We then use watershed segmentation to delineate cells, and apply energy minimizing functions to refine the delineation. Differential motion activity feature maps are computed to detect moving cells. This stage is then followed by cell cluster detection and separation. We validated our method on datasets of image sequences of live cells and reference masks from the Cell Tracking Challenge (CTC) consortium. Our methodology produced promising detection and segmentation results over image sequences varying in size, shape, illumination and dyeing techniques.

Recent advances in quantitative microscopy for the life sciences have enabled experimental biologists to probe cells with unprecedented resolution and speed. At the same time, the AI revolution has dramatically increased the amount of information that can be extracted from multidimensional microscopy data. However, the large amount of data generated and the complexity of state-of-the-art AI models pose a severe bottleneck at the image analysis stage. Cell-ACDC is an open-source, user-friendly software that provides a powerful end-to-end solution for segmentation, tracking, and quantitative analysis of single cells in multidimensional microscopy data. It is tailored for experimental biologists who may lack the advanced technical expertise required to implement such models. This article shows how to utilize the framework to easily leverage the most recent models, along with many tools for smart and semi-automated data correction, to maximize the amount of biological information obtainable. Cell-ACDC supports multi-channel, time-lapse, and z-stack microscopy data, and provides a dedicated toolset tailored to each type of data dimensionality. Because of its modular design, which allows new models to be seamlessly integrated and directly accessed by biologists, Cell-ACDC has the potential to serve as a reference tool for microscopy data analysis.

Cell segmentation is a common step in cell behavior analysis. Reliably and automatically segmenting cells in microscopy images remains challenging, especially in differential inference contrast microscopy images and phase-contrast microscopy images. In this paper, we propose a deep learning solution combining a Mask RCNN architecture with Shape-Aware Loss to produce cell instance segmentation. Our approach outperforms prior works in cell segmentation, achieving an IOU of 91.91% on the DIC-C2DH-HeLa dataset and an IOU of 94.93 % on the PhC-C2DH-U373 dataset. Our framework can calculate cell instance segmentation masks from both types of microscopy images without any additional post-processing.Clinical Relevance - The proposed approach produces accurate instance segmentation in Differential Inference Contrast and Phase-Contrast microscopy images. The segmentation results can be reliably used in cell behavior analysis and cell tracking.

The lacking of automatic screen systems that can deal with large volume of time-lapse optical microscopy imaging is a bottleneck of modern bio-imaging research. In this paper, we propose an effective automated analytic system that can be used to acquire, track and analyze cell-cycle behaviors of a large population of cells. We use traditional watershed algorithm for cell nuclei segmentation and then a novel hybrid merging method is proposed for fragments merging. After a distance and size based tracking procedure, the performance of fragments merging is improved again by the sequence context information. At last, the cell nuclei can be classified into different phases accurately in a continuous hidden Markov model (HMM). Experimental results show the proposed system is very effective for cell sequence segmentation, tracking and cell phase identification.