A benchmark for comparison of cell tracking algorithms (original) (raw)
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Joint cell segmentation and tracking using cell proposals
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016
Time-lapse microscopy imaging has advanced rapidly in last few decades and is producing large volume of data in cell and developmental biology. This has increased the importance of automated analyses, which depend heavily on cell segmentation and tracking as these are the initial stages when computing most biologically important cell properties. In this paper, we propose a novel joint cell segmentation and tracking method for fluorescence microscopy sequences, which generates a large set of cell proposals, creates a graph representing different cell events and then iteratively finds the most probable path within this graph providing cell segmentations and tracks. We evaluate our method on three datasets from ISBI Cell Tracking Challenge and show that our greedy nonoptimal joint solution results in improved performance compared with state of the art methods.
The Cell Tracking Challenge: 10 years of objective benchmarking
Nature Methods
The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions ...
Tracking-Assisted Segmentation of Biological Cells
Cornell University - arXiv, 2019
U-Net and its variants have been demonstrated to work sufficiently well in biological cell tracking and segmentation. However, these methods still suffer in the presence of complex processes such as collision of cells, mitosis and apoptosis. In this paper, we augment U-Net with Siamese matching-based tracking and propose to track individual nuclei over time. By modelling the behavioural pattern of the cells, we achieve improved segmentation and tracking performances through a re-segmentation procedure. Our preliminary investigations on the Fluo-N2DH-SIM+ and Fluo-N2DH-GOWT1 datasets demonstrate that absolute improvements of up to 3.8 % and 3.4% can be obtained in segmentation and tracking accuracy, respectively.
Medical image analysis, 2018
We present a novel computational framework for the analysis of high-throughput microscopy videos of living cells. The proposed framework is generally useful and can be applied to different datasets acquired in a variety of laboratory settings. This is accomplished by tying together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. In contrast to most existing approaches, which aim to be general, no assumption of cell shape is made. Spatial, temporal, and cross-sectional variation of the analysed data are accommodated by two key contributions. First, time series analysis is exploited to estimate the temporal cell shape uncertainty in addition to cell trajectory. Second, a fast marching (FM) algorithm is used to integrate the inferred cell properties with the observed image measurements in order to obtain image likelihood for cell segmentation, and association. The proposed approach has been tested ...
Advances in Intelligent Systems and Computing, 2017
Recent developments in live-cell microscopy imaging have led to the emergence of Single Cell Biology. This field has also been supported by the development of cell segmentation and tracking algorithms for data extraction. The validation of these algorithms requires benchmark databases, with manually labeled or artificially generated images, so that the ground truth is known. To generate realistic artificial images, we have developed a simulation platform capable of generating biologically inspired objects with various shapes and size, which are able to grow, divide, move and form specific clusters. Using this platform, we compared four tracking algorithms: Simple Nearest-Neighbor (NN), NN with Morphology (NNm) and two DBSCAN-based methodologies. We show that Simple NN performs well on objects with small velocities, while the others perform better for higher velocities and when objects form clusters. This platform for benchmark images generation and image analysis algorithms testing is openly available at (http://griduni.uninova.pt/Clustergen/ClusterGen\_v1.0.zip).
Cell segmentation and tracking via proposal generation and selection
2017
Biology and medicine rely heavily on images to understand how the body functions, for diagnosing diseases and to test the effects of treatments. In recent decades, microscopy has experienced rapid improvements, enabling imaging of fixed and living cells at higher resolutions and frame rates, and deeper inside the biological samples. This has led to rapid growth in the image data. Automated methods are needed to quantitatively analyze these huge datasets and find statistically valid patterns. Cell segmentation and tracking is critical for automated analysis, yet it is a challenging problem due to large variations in cell shapes and appearances caused by various factors, including cell type, sample preparation and imaging setup. This thesis proposes novel methods for segmentation and tracking of cells, which rely on machine learning based approaches to improve the performance, generalization and reusability of automated methods. Cell proposals are used to efficiently exploit spatial a...
Algorithms
Background: Time-lapse microscopy imaging is a key approach for an increasing number of biological and biomedical studies to observe the dynamic behavior of cells over time which helps quantify important data, such as the number of cells and their sizes, shapes, and dynamic interactions across time. Label-free imaging is an essential strategy for such studies as it ensures that native cell behavior remains uninfluenced by the recording process. Computer vision and machine/deep learning approaches have made significant progress in this area. Methods: In this review, we present an overview of methods, software, data, and evaluation metrics for the automatic analysis of label-free microscopy imaging. We aim to provide the interested reader with a unique source of information, with links for further detailed information. Results: We review the most recent methods for cell segmentation, event detection, and tracking. Moreover, we provide lists of publicly available software and datasets....
Analysis of High-throughput Microscopy Videos: Catching Up with Cell Dynamics
Lecture Notes in Computer Science, 2015
We present a novel framework for high-throughput cell lineage analysis in time-lapse microscopy images. Our algorithm ties together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. The proposed contribution exploits the Kalman inference problem by estimating the time-wise cell shape uncertainty in addition to cell trajectory. These inferred cell properties are combined with the observed image measurements within a fast marching (FM) algorithm, to achieve posterior probabilities for cell segmentation and association. Highly accurate results on two different cell-tracking datasets are presented.
Online Neural Cell Tracking Using Blob-Seed Segmentation and Optical Flow
2019
Existing neural cell tracking methods generally use the morphology cell features for data association. However, these features are limited to the quality of cell segmentation and are prone to errors for mitosis determination. To overcome these issues, in this work we propose an online multi-object tracking method that leverages both cell appearance and motion features for data association. In particular, we propose a supervised blob-seed network (BSNet) to predict the cell appearance features and an unsupervised optical flow network (UnFlowNet) for capturing the cell motions. The data association is then solved using the Hungarian algorithm. Experimental evaluation shows that our approach achieves better performance than existing neural cell tracking methods.
Cell segmentation for division rate estimation in computerized video time-lapse microscopy
Multimodal Biomedical Imaging II, 2007
The automated estimation of cell division rate plays an important role in the evaluation of a gene function in high throughput biomedical research. Automatic segmentation and cell counting of phase contrast images, which are widely used in cell cultures when colorless cell specimen was observed with high magnifications, is a challengeable task because of low contrast boundary on membranes and similar intensities between cell inside areas and outside areas. In this paper we propose a fully automatic system for phase contrast cell image segmentation. Our system consists of three steps: cell detection, foreground segmentation and individual cell segmentation. Cell detection is done by AdaBoost algorithm, and the detected cells are used as seeds for cell segmentation. Boosting is an effective way to select discriminative features. Dark dots in the nuclei and white boundaries around the membranes are detected by the Mexican Hat Wavelet, and these features are selected by AdaBoost algorithm to generate a robust detector.