Leukocyte Tracking Database, a collection of immune cell tracks from intravital 2-photon microscopy videos.
ABSTRACT: Recent advances in intravital video microscopy have allowed the visualization of leukocyte behavior in vivo, revealing unprecedented spatiotemporal dynamics of immune cell interaction. However, state-of-the-art software and methods for automatically measuring cell migration exhibit limitations in tracking the position of leukocytes over time. Challenges arise both from the complex migration patterns of these cells and from the experimental artifacts introduced during image acquisition. Additionally, the development of novel tracking tools is hampered by the lack of a sound ground truth for algorithm validation and benchmarking. Therefore, the objective of this work was to create a database, namely LTDB, with a significant number of manually tracked leukocytes. Broad experimental conditions, sites of imaging, types of immune cells and challenging case studies were included to foster the development of robust computer vision techniques for imaging-based immunological research. Lastly, LTDB represents a step towards the unravelling of biological mechanisms by video data mining in systems biology.
Project description:The accurate tracking of zebrafish larvae movement is fundamental to research in many biomedical, pharmaceutical, and behavioral science applications. However, the locomotive characteristics of zebrafish larvae are significantly different from adult zebrafish, where existing adult zebrafish tracking systems cannot reliably track zebrafish larvae. Further, the far smaller size differentiation between larvae and the container render the detection of water impurities inevitable, which further affects the tracking of zebrafish larvae or require very strict video imaging conditions that typically result in unreliable tracking results for realistic experimental conditions. This paper investigates the adaptation of advanced computer vision segmentation techniques and multiple object tracking algorithms to develop an accurate, efficient and reliable multiple zebrafish larvae tracking system. The proposed system has been tested on a set of single and multiple adult and larvae zebrafish videos in a wide variety of (complex) video conditions, including shadowing, labels, water bubbles and background artifacts. Compared with existing state-of-the-art and commercial multiple organism tracking systems, the proposed system improves the tracking accuracy by up to 31.57% in unconstrained video imaging conditions. To facilitate the evaluation on zebrafish segmentation and tracking research, a dataset with annotated ground truth is also presented. The software is also publicly accessible.
Project description:Digitization of video recordings often requires the laborious procedure of manually clicking points of interest on individual video frames. Here, we present progressive tracking, a procedure that facilitates manual digitization of markerless videos. In contrast to existing software, it allows the user to follow points of interest with a cursor in the progressing video, without the need to click. To compare the performance of progressive tracking with the conventional frame-wise tracking, we quantified speed and accuracy of both methods, testing two different input devices (mouse and stylus pen). We show that progressive tracking can be twice as fast as frame-wise tracking while maintaining accuracy, given that playback speed is controlled. Using a stylus pen can increase frame-wise tracking speed. The complementary application of the progressive and frame-wise mode is exemplified on a realistic video recording. This study reveals that progressive tracking can vastly facilitate video analysis in experimental research.
Project description:Optical mapping is a high-resolution fluorescence imaging technique, which provides highly detailed visualizations of the electrophysiological wave phenomena, which trigger the beating of the heart. Recent advancements in optical mapping have demonstrated that the technique can now be performed with moving and contracting hearts and that motion and motion artifacts, once a major limitation, can now be overcome by numerically tracking and stabilizing the heart's motion. As a result, the optical measurement of electrical activity can be obtained from the moving heart surface in a co-moving frame of reference and motion artifacts can be reduced substantially. The aim of this study is to assess and validate the performance of a 2D marker-free motion tracking algorithm, which tracks motion and non-rigid deformations in video images. Because the tracking algorithm does not require markers to be attached to the tissue, it is necessary to verify that it accurately tracks the displacements of the cardiac tissue surface, which not only contracts and deforms, but also fluoresces and exhibits spatio-temporal physiology-related intensity changes. We used computer simulations to generate synthetic optical mapping videos, which show the contracting and fluorescing ventricular heart surface. The synthetic data reproduces experimental data as closely as possible and shows electrical waves propagating across the deforming tissue surface, as seen during voltage-sensitive imaging. We then tested the motion tracking and motion-stabilization algorithm on the synthetic as well as on experimental data. The motion tracking and motion-stabilization algorithm decreases motion artifacts approximately by 80% and achieves sub-pixel precision when tracking motion of 1-10 pixels (in a video image with 100 by 100 pixels), effectively inhibiting motion such that little residual motion remains after tracking and motion-stabilization. To demonstrate the performance of the algorithm, we present optical maps with a substantial reduction in motion artifacts showing action potential waves propagating across the moving and strongly deforming ventricular heart surface. The tracking algorithm reliably tracks motion if the tissue surface is illuminated homogeneously and shows sufficient contrast or texture which can be tracked or if the contrast is artificially or numerically enhanced. In this study, we also show how a reduction in dissociation-related motion artifacts can be quantified and linked to tracking precision. Our results can be used to advance optical mapping techniques, enabling them to image contracting hearts, with the ultimate goal of studying the mutual coupling of electrical and mechanical phenomena in healthy and diseased hearts.
Project description:We describe a new method for imaging leukocytes in vivo by exciting the endogenous protein fluorescence in the ultraviolet (UV) spectral region where tryptophan is the major fluorophore. Two-photon excitation near 590 nm allows noninvasive optical sectioning through the epidermal cell layers into the dermis of mouse skin, where leukocytes can be observed by video-rate microscopy to interact dynamically with the dermal vascular endothelium. Inflammation significantly enhances leukocyte rolling, adhesion, and tissue infiltration. After exiting the vasculature, leukocytes continue to move actively in tissue as observed by time-lapse microscopy, and are distinguishable from resident autofluorescent cells that are not motile. Because the new method alleviates the need to introduce exogenous labels, it is potentially applicable for tracking leukocytes and monitoring inflammatory cellular reactions in humans.
Project description:Video recording is now ubiquitous in the study of animal behavior, but its analysis on a large scale is prohibited by the time and resources needed to manually process large volumes of data. We present a deep convolutional neural network (CNN) approach that provides a fully automated pipeline for face detection, tracking, and recognition of wild chimpanzees from long-term video records. In a 14-year dataset yielding 10 million face images from 23 individuals over 50 hours of footage, we obtained an overall accuracy of 92.5% for identity recognition and 96.2% for sex recognition. Using the identified faces, we generated co-occurrence matrices to trace changes in the social network structure of an aging population. The tools we developed enable easy processing and annotation of video datasets, including those from other species. Such automated analysis unveils the future potential of large-scale longitudinal video archives to address fundamental questions in behavior and conservation.
Project description:We have developed software for fully automated tracking of vibrissae (whiskers) in high-speed videos (>500 Hz) of head-fixed, behaving rodents trimmed to a single row of whiskers. Performance was assessed against a manually curated dataset consisting of 1.32 million video frames comprising 4.5 million whisker traces. The current implementation detects whiskers with a recall of 99.998% and identifies individual whiskers with 99.997% accuracy. The average processing rate for these images was 8 Mpx/s/cpu (2.6 GHz Intel Core2, 2 GB RAM). This translates to 35 processed frames per second for a 640 px×352 px video of 4 whiskers. The speed and accuracy achieved enables quantitative behavioral studies where the analysis of millions of video frames is required. We used the software to analyze the evolving whisking strategies as mice learned a whisker-based detection task over the course of 6 days (8148 trials, 25 million frames) and measure the forces at the sensory follicle that most underlie haptic perception.
Project description:<h4>Motivation</h4>Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark.<h4>Results</h4>The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately.<h4>Availability and implementation</h4>The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge.
Project description:Automated tracking of living cells in microscopy image sequences is an important and challenging problem. With this application in mind, we propose a global track linking algorithm, which links cell outlines generated by a segmentation algorithm into tracks. The algorithm adds tracks to the image sequence one at a time, in a way which uses information from the complete image sequence in every linking decision. This is achieved by finding the tracks which give the largest possible increases to a probabilistically motivated scoring function, using the Viterbi algorithm. We also present a novel way to alter previously created tracks when new tracks are created, thus mitigating the effects of error propagation. The algorithm can handle mitosis, apoptosis, and migration in and out of the imaged area, and can also deal with false positives, missed detections, and clusters of jointly segmented cells. The algorithm performance is demonstrated on two challenging datasets acquired using bright-field microscopy, but in principle, the algorithm can be used with any cell type and any imaging technique, presuming there is a suitable segmentation algorithm.
Project description:PURPOSE:Endoscopic examinations are frequently-used procedures for patients with head and neck cancer undergoing radiotherapy, but radiation treatment plans are created on computed tomography (CT) scans. Image registration between endoscopic video and CT could be used to improve treatment planning and analysis of radiation-related normal tissue toxicity. The purpose of this study was to explore the feasibility of endoscopy-CT image registration without prospective physical tracking of the endoscope during the examination. METHODS:A novel registration technique called Location Search was developed. This technique uses physical constraints on the endoscope's view direction to search for the virtual endoscope coordinates that maximize the similarity between the endoscopic video frame and the virtual endoscopic image. Its performance was tested on phantom and patient images and compared to an established registration technique, Frame-To-Frame Tracking. RESULTS:In phantoms, Location Search had average registration errors of 0.55 ± 0.60 cm for point measurements and 0.29 ± 0.15 cm for object surface measurements. Frame-To-Frame Tracking achieved similar results on some frames, but it failed on others due to the virtual endoscope becoming lost. This weakness was more pronounced in patients, where Frame-To-Frame tracking could not make it through the nasal cavity. On successful patient video frames, Location Search was able to find endoscope positions with an average distance of 0.98 ± 0.53 cm away from the ground truth positions. However, it failed on many frames due to false similarity matches caused by anatomical structural differences between the endoscopic video and the virtual endoscopic images. CONCLUSIONS:Endoscopy-CT image registration without prospective physical tracking of the endoscope is possible, but more development is required to achieve an accuracy suitable for clinical translation.
Project description: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.