Initially, an animal having fluorescent cells, is anaesthetized, and prepared for imaging by immobilization and surgical exposition of the organ of interest Fig. Then, 4D data, composed of parallel image planes at different depths, are acquired for several time instants Fig. After acquisition, data are analysed by detecting cells Fig.
The described pipeline was used to generate all the entries proposed in the current work Fig. Subsequently, right centroids are associated over time producing the cell tracks. For each phase of the imaging pipeline are reported specific problematics that affect cell tracking. Challenges are introduced at each stage of the previously described pipeline and arise both from the complex biomechanical properties of leukocytes and from technical artifacts of in vivo imaging Fig.
More specifically, high plasticity of cell shape, sustained speed and frequent contacts, set a limit on the capacity of detecting and tracking cells for long time periods Additionally, technical artifacts such as the variation and non-uniform diffraction of the light emitted by fluorescently- tagged cells or the physiological movement of the sample due to peristalsis, breathing or pulsing of blood vessels, further challenge the automatic analysis.
Therefore, additional steps such as image pre-processing, tuning of software parameters and manual curation of tracks, are required to improve tracking results. As a consequence, usability of imaging software is reduced 13 , bias introduced and the reproducibility of the results is compromised. An example is provided in Fig. Arrows indicate cell displacement.
Automatic tracks were interrupted when the software could not detect or link cells, yielding to the creation of an increased number of shorter tracklets. Providing the scientific community with datasets interpreted by experts is essential to foster the development of data science methods. To this end, international cell tracking challenges on public datasets 14 , 15 allowed to highlight the properties amongst different algorithms. However, the provided datasets did not include leukocytes observed by intravital imaging. For this reason, it is necessary to develop an extended dataset of MP-IVM videos, where a significant number of leukocytes are tracked.
Here we present a leukocyte tracking database, namely "LTDB", that includes MP-IVM videos of immune cells, together with their relative tracks which were manually annotated by experts. Each video contains one or more challenges for the automatic analysis Table 2 available online only , and captured the behaviour of one or more cell populations Table 3 in response to different stimuli Table 4.
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All the videos and tracks are made available as individual files or as a spatio-temporal database Fig. Videos, metadata and the position over time of tracked cells are organized as the conceptual Entity-Relationship model described in a , corresponding to the logical database optimized version depicted in b. A video belongs to a Collection, depicts one or more Problematic and includes an Image series. The Image entity is double-framed because it is a weak entity, which depends on the Video entity.
A Cell has one type and one unique identifier.
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One Video tracks one or more Cell, every Cell being depicted by the Track association at a given timestamp t and in a spatial position x, y, z of that Video. The VisibleIn association further describes the channel of the video in which a cell is visible. The logical database is derived from the conceptual model and then optimized for read-access. Imaging data are logically stored as TIFF image series with a specific filename.
The expected usage of LTDB is to serve as a ground truth for the validation of tracking algorithms Fig. Differences with respect to the ground truth can be evaluated using, for instance, a metric that accounts for complete tracking graph comparison LTDB videos are provided as input to a tracking algorithm.
Computed tracks can be compared with respect to the ground-truth tracks using a methodology of choice such as the complete graph comparison 15 , In the example red arrows indicate errors where a cell was detected not sufficiently close, and when a track was interrupted. LTDB videos and tracks can potentially be used in the context of supervised machine learning as training and validation dataset. The generated predictive model can be generalized and used to track new videos. Properties of leukocyte migration in different experimental conditions can potentially be discovered by the application of pattern recognition on LTDB metadata and tracks.
LTDB further aims at being a training dataset for supervised machine learning methods. Indeed, in light of the recent application of deep learning for object detection and tracking in highly variable scenarios 17 — 19 , LTDB can provide the large number of images-tracks pairs required for the training of predictive models Fig.
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In this case, broad imaging conditions may support the generalization capabilities of these methods. Although LTDB was provided to primarily enhance tracking algorithms, the database embeds biomedical knowledge. To this end, data-mining and image-based systems biology methods can be applied to correlate images, tracks and metadata for investigating properties of the immune system in health and disease Fig. Experiments were performed by four research groups using three customized two-photon microscopy platforms Table 5.
Either the splenic or the lymph-node surgical models were used for acquisition Fig. Videos were acquired from 26 unique experiments, to observe the interplay of neutrophils, B cells, T cells and natural killer cells in innate or adaptive immune responses Table 4. No image processing was applied to the provided videos.
RAW images were also used for manual tracking. Centroids of cells were manually annotated and linked over time, using the "Spots drawing" tool from Imaris Bitplane. This process was performed by a group of three operators who tracked all the cells independently, redundantly and in three different locations without seeing the results produced by each other. In order to maximize track duration, cells were tracked also if partially visible.
Tracks were interrupted only when cells completely disappeared. For specific studies, tracks of partially visible cells, migrating close to the boundaries of the field of view, can be excluded a posteriori by the user. Multiple independent annotations and tracks were merged into the consensus ground truth provided along with the dataset using a majority-voting scheme. This process was performed manually by a fourth expert using the "Unify" functionality of Imaris. Two tracks were said likely to "match" i.
N was defined as the minimum between the track duration and Conflictive situations were detected as tracks matching for only certain time instants but not for the entire track duration. Tracks with a duration shorter than 4 time instants were also inspected manually. Due to the high plasticity of cells these criteria were used only to facilitate the work of the fourth expert who had to manually merge multiple tracks as follows: If at least two operators agreed on the direction of a cell, the track was included in the dataset i. If two operators tracked a cell, but the track duration was different, the points annotated only by one operator were evaluated, confirmed or discarded by the fourth expert.
When two operators could not agree on the direction of a cell, the following method was applied. If the fourth expert or the Matlab script identified an evident tracking error i. For real conflictive situations i. If still the majority consensus could not be reached, and only in this case, tracks were interrupted. Finally, the position of cell centroids included in the ground truth was not averaged but selected as the centroid closer to the mean. Although this choice may produce less smooth tracks, it avoids to position a centroid outside non-convex cells.
These criteria together with the manual merging of tracks and re-evaluation of tracking conflicts, allowed to include the maximum number of tracks for the longest possible period of time. The mouse strains included in this study are specified in Table 6 available online only. Data included in this work Videos and Tracks are available through figshare Data Citation 1. The following supplementary files are available through figshare Data Citation 1. Imaging data were captured from organs of living animals using either the splenic or the popliteal lymph node surgical models Fig.
Cells involved in both innate and adaptive responses were included in the dataset. To represent data generated by multiple laboratories in different experimental settings 22 , LTDB includes videos with different size, resolution, sampling rate and challenges for the automatic analysis Table 2 available online only , acquired by three different microscopy platforms Table 5. Moreover, cells were labelled with different fluorescent tags and detected by multiple channels Table 3.
The following measures were computed to estimate the complexity of each video: signal to noise ratio SNR , minimum distance between two cells Dist and number of cells per time instant. Since the proposed dataset is centroid-based rather than segmentation-based, SNR was estimated by adapting the definitions proposed in 15 with the following heuristic. Let c i,t be the centroid position of cell i at time t. Then, considering a typical cell diameter of 10 um , each voxel v was defined as foreground FG-inside a cell or background BG-outside a cell according with Equation 1.
This assumption allowed to sample a sufficient number of points in each video to estimate the aforementioned measures. Table 2 available online only summarizes the average values of each video while the additional script EstimateDSMeasures. The consensus tracking ground truth provided with LTDB includes unique tracks composed of instantaneous annotations. On average, each track is composed by 61 annotations. This varying with the track duration. The total observation time included in LTDB amounts to the equivalent of hours for a single cell.
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Common tracking errors i. Individual operators produced tracks annotations which were merged into the tracks of the consensus tracking ground truth.
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The performances of each operator with respect to the consensus ground truth is reported in Table 7. To this end, the TRA 15 measure was computed.
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This measure includes a complete comparison of tracks represented as an acyclic oriented graph In order to estimate this measure, the ground truth and the individual tracks were converted in the format described in 15 and evaluated using the TRAMeasure software provided along. Being our dataset centroid-based a difference of 1 voxel would made the matching not possible. Hence, considering the typical cell diameter, we approximated a sphere around each of the centroids.