We developed a new pipeline, 3DeeCellTracker, which integrates novel and existing techniques (Figure 1A). After preprocessing (see Materials and methods), it performs automatic segmentation of cells in all 3D + T images using 3D U-Net for classifying individual voxels into cell or non-cell categories (Ronneberger et al., 2015; Çiçek et al., 2016). Continuous regions of ‘cell’ voxels are separated into individual cell regions using the watershed method (Beucher and Meyer, 1993), and then numbered. The segmented cells are manually corrected only in the first volume of 3D images. In the following 3D tracking step, we considerably increased the efficiency by introducing a deep learning technique, feedforward network (FFN), to predict cell positions based on spatial patterns of cells maintained between previous and current images. The predicted positions are corrected with a non-rigid point set registration method called PR-GLS (Ma et al., 2016) and by our custom method to obtain precise cell locations, which are critical for extracting the accurate dynamics of cellular signals. The 3D U-Net and the FFN are pre-trained using manually confirmed data or synthetic data (Figure 1B,C) from a single 3D image volume. The tracking results were visually inspected by comparing the locations of tracked cells with the corresponding raw images (Figure 1—figure supplement 2). The keys to our method are the use of simulation to produce large amounts of training data for the FFN and the carefully designed post-processing methods for the FFN, which result in the flexible and robust tracking of moving cells in very different 3D + T datasets. In the following two sections, we describe details of the segmentation and tracking method.

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For segmentation, cell-like regions in an image should be segmented into individual cells which may differ in intensities, sizes, shapes, and textures. Cell segmentation in 2D images using deep networks has been previously reported (Ronneberger et al., 2015; Van Valen et al., 2016; Bannon et al., 2018). In this study, we utilized a deep network called 3D U-Net to segment cells in 3D images to predict the class labels (cell or non-cell) of individual voxels based on information contained in neighboring voxels (Figure 2A and Figure 2—figure supplement 1; Ronneberger et al., 2015; Çiçek et al., 2016). The U-Net can generate precise segmentation under diverse imaging conditions and can be trained with very few annotated images (e.g. only one 3D image volume in this study; see Figure 1B).

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The pre-processed images of the first volume are used to train the 3D U-Net (Figure 1B), and the trained U-net is then used for the segmentation of cells in all the following volumes. Once trained on one dataset, the 3D U-Net can be directly reused for different datasets obtained under similar optical conditions. The cell-like regions detected using 3D U-Net are grouped and separated into individual cells using the watershed method (see Figure 2A and Materials and methods).

For tracking cells, two major strategies can be considered. One strategy is to utilize the information contained in each cell region, for example, local peaks or distributions of intensities (Toyoshima et al., 2016). Using this information, a program can update the position of each cell by searching for its new position in nearby regions in the next volume. However, the obvious drawback of this strategy is that cells can be mistracked if their movements are comparable to or greater than the distances between adjacent cells (see below).

Another strategy is to represent cells by their center points, ignoring the information in each cell region and treating the cells as a set of points. In this strategy, the new positions of a cell set in the next volume can be estimated based on the patterns of their spatial distributions, and cells with large movements can also be tracked based on the global trend of the movements. Previously reported methods utilizing this strategy, called point set registration (Ma et al., 2016; Jian and Vemuri, 2005; Myronenko et al., 2006), used the spatial distributions and coherency of movements to track points within artificial datasets. However, the spatial patterns are conventionally characterized by features designed by experts, an approach that did not work well for the cell images used in this study (refer to the results below concerning Fast Point Feature Histograms [FPFH; Rusu et al., 2009] versus FFN). Another problem with this strategy is that the estimated positions are not always accurate because the information contained in each cell region is ignored (see the results below concerning our method for accurate correction).

In order to obtain more robust and accurate tracking results, we integrated the spatial pattern (i.e. point sets) and local cell region strategies and used a deep learning technique, FFN, for the former. We used FFN to match temporally adjacent cells based on the distance pattern between each cell and its 20 surrounding cells (Figure 2B). Using the pattern obtained by the FFN, all the cells at t₁ are compared with all the cells at t₂, and the most similar ones are regarded as the same cells at t₁ and t₂, a process we call initial matching.

Although deep network techniques are expected to produce superior cell tracking results, the method had not been used previously because it requires large amounts of training data. These data are difficult to prepare manually, especially for 3D + T images, as validating and correcting large numbers of cell positions over time by switching multiple layers along the z axis is virtually impossible. To solve this difficulty in preparing training data for FFN, we generated >500,000,000 synthetic training data points by simulating cell movements (see Figure 2—figure supplement 2 and Materials and methods).

In the pipeline, the center points of cells are extracted from the cell regions segmented by 3D U-Net and the watershed method, and a pre-trained FFN (Figures 1C and 2B) is applied to the cell points to generate the initial matching from volumes t to t+1 (Figure 2C, panels 2–2 and 4–1). To improve the initial matching, a non-rigid point set registration method (PR-GLS) (Ma et al., 2016) is used to generate a coherent transformation, that is, neighboring cells should have similar movements (Figure 2C, panel 4–2). Originally, PR-GLS was used in conjunction with the FPFH method to predict cell positions (Ma et al., 2016); however, our FFN generates more accurate initial matchings than FPFH (Figure 2—figure supplement 3A and B), and our combination of FFN + PR-GLS generates more accurate predictions of cell positions than the FPFH + PR-GLS or the classic affine alignment (Myronenko et al., 2006) does (Figure 2—figure supplement 3C and D). Nevertheless, our method sometimes generates subtle errors because some cells may show slightly different movements from those of their neighboring cells, which can accumulate over time. To overcome these errors, the estimated positions are accurately corrected to compensate for their differences by utilizing information from local cell regions contained in the 3D U-Net output (Figure 2C, panel 5, Figure 2—figure supplement 4, and Materials and methods).

To test the performance of 3DeeCellTracker, we analyzed 3D + T images of neurons in the deforming brain of C. elegans. C. elegans has been used as a model for imaging all neuronal activities in the brain (‘whole brain imaging’) owing to its small brain (~40 µm³ in an adult) in a transparent body, the complete description of all connections of 302 neurons, feasibility in the use of genetically encoded calcium indicators (GECI), and the capability of perception, memory, and decision-making in its small brain (de Bono and Maricq, 2005). Whole brain imaging of C. elegans has been reported from several laboratories, and the most popular optical system currently is the spinning disk confocal system, for which each laboratory has developed their own tracking software (Schrödel et al., 2013; Toyoshima et al., 2016; Venkatachalam et al., 2016; Nguyen et al., 2017). We established our own spinning disk confocal system for whole-brain imaging, OSB-3D (see Materials and methods for details), and obtained 3D + T images of whole brain activity from a strain established in our laboratory and from the one used in a previous study by Nguyen et al., 2016 (datasets worm #1 and #2, respectively). In addition, we analyzed the whole-brain images published previously using a different optical system (Toyoshima et al., 2016; dataset worm #3). In all datasets, the worms were semi-immobilized either by anesthetization (worm #1 and #2) or by constriction (worm #3), and red fluorescent proteins and GECI were used to monitor cell positions and cell activities, respectively.

After segmentation, we manually confirmed 164 neurons in the head in the first volume of 3D + T images, in which the distribution of cell signals was mostly, but not completely, separated from the background signals (dataset worm #1a; see Figure 3A–C). While the worm was anesthetized, its head deformed and the cells moved during imaging. Our method tracked all the neurons in all 171 volumes except for those that moved out of the field of view of the camera (Figure 3D–F, Figure 3—video 1).

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To evaluate the difficulty in tracking cells with large movements, we calculated a score for the relative movements (RM), which is the movement of a cell divided by the distance from that cell to its closest neighboring cell. When RM is small, searching for the closest cell is the simplest method to find the new position of a given cell in the next volume. However, such a simple approach may lead to tracking errors when RM ≥0.5 (Figure 4 and Materials and methods). Although most of the cell RM values were ≤0.5 in the worm #1 dataset (Figure 3B, bottom), other datasets had cells with RM ≥0.5; nevertheless, these were also successfully tracked by our program (see below).

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We also tested worm #1b dataset, obtained from the same strain with worm #1a (Figure 3—figure supplement 1), in which we again achieved 100% tracking accuracy without changing any parameters aside from the noise level (see Tables 1 and 2). The positional information of cell nuclei in both datasets was then used to extract the calcium dynamics of the neurons based on GECI intensities, which reflect spontaneous activities of neurons in the worm's brain (Figure 5, Figure 5—figure supplement 1 and Figure 5—video 1).

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The same method was applied to 3D + T images obtained using the same OSB-3D system in our laboratory but from the neurons of a worm strain used in a previous whole brain imaging study (Nguyen et al., 2016; AML14 strain, dataset worm #2; Figure 3—figure supplement 2A). Our method again achieved a 100% accuracy of tracking and extracted calcium signals from all 101 neurons even though this dataset differs considerably from the worm #1 dataset in terms of nuclear marker intensity and movement patterns (Figure 3—figure supplement 2A–F, Figure 3—video 2 and Figure 5—video 2). It should be noted that more neurons were detected using our method than using the original method (approximately 90 neurons or less) (Nguyen et al., 2016).

We also applied our method to a publicly available dataset, which was obtained using a different strain, a different microscopy setup, and different imaging conditions from worm #1 and #2 datasets (worm #3, Figure 3—figure supplement 3A; Toyoshima et al., 2016). In this case, the worm was loosely constrained in a microfluidic device and not anesthetized, thus exhibited larger head deformations and cell displacements between volumes (by a factor of approximately three times) compared to the worm #1 and #2 datasets (Figure 3—figure supplement 3A and B). In addition, this dataset had a lower resolution (half the resolution in the x and y directions). Nevertheless, with a few parameter modifications (Table 2 and Materials and methods), our method correctly tracked 171/175 (=98%) of the neurons (Figure 3—figure supplement 3C–F and Figure 3—video 3). Our result was comparable to that of the original report, in which 171 out of 198 neurons were tracked without error (Toyoshima et al., 2016). These results indicate that our method can flexibly process 3D + T images of neurons in a semi-immobilized worm's brain obtained under different conditions.

To reveal the relationships between neuronal activities and animal behavior, Nguyen et al., developed a method to track neurons in a freely moving worm, in which the deformation of the brain and the movements of neurons are considerably larger than those occurring in semi-immobilized worms (Nguyen et al., 2016; Nguyen et al., 2017). After ‘straightening’ the worm and segmentation of its neurons, they made a set of reference volumes to which each neuron was matched to assign its identity. Although this method is powerful, it requires a high-performance scientific computing cluster running on up to 200 cores simultaneously. We therefore tested whether the 3DeeCellTracker implemented on a desktop PC could process such a challenging dataset (worm #4).

With a few modifications (see Materials and methods), we used 3DeeCellTracker to segment and track 113 cells in the initial 500 volumes from the worm #4 dataset. Here we analyzed the images preprocessed by the straightening method (Nguyen et al., 2017), which is necessary for our current method. Even after the straightening, the cell movements were quite large, with many comparable to the distances between cells, that is, RM ≥0.5 (Figure 6A and B). We visually inspected the tracking of 90 cells while the remaining 23 were not checked because of difficulties arising from weak intensities/photobleaching and/or the cells being in dense regions. Note that 70 cells were inspected in the original report (Nguyen et al., 2017). We found that 66 out of 90 cells (73%) were correctly tracked without errors (Figure 6D–F, single mode), which is acceptable but not ideal.

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To further improve our method for the larger cell movements, we developed a new mode, in which multiple predictions of cell positions are made from different previous time points, and the final prediction is taken as the average of these predictions. We call this ‘ensemble mode’ and the previous method ‘single mode’, wherein the predictions of cell positions at time t are derived from the results at t-1 (Figure 6C).

When applying the ensemble mode, we again found 66 cells correctly tracked without errors, and that the remaining 24 cells were correctly tracked in most volumes (94%–98.2%). This was a substantial improvement over the single mode, in which errors at t are maintained until the end of tracking (Figure 6D–F, ensemble mode; Figure 6—video 1). In total, the ensemble mode correctly tracked 44,905 out of 45,000 cell movements (99.8%), a result at least comparable to that in the original report (Nguyen et al., 2017). From the trajectories of two example cells, we found that large movements, including ones along the z-axis (across ~30 layers), occurred frequently (Figure 6G), demonstrating the excellent performance of our method on a desktop PC in this challenging dataset containing considerably large scale movements. However, because the ensemble mode requires longer times for the tracking than the single mode (10 versus 2 min/volume, respectively), we used the single mode in the following analyses.

To test the general applicability of the 3DeeCellTracker, we applied our method to the 3D + T images of a naturally beating heart in a zebrafish larva obtained at 100 volumes per second using a substantially different optical system, the SCAPE 2.0 system (Voleti et al., 2019; Figure 7A). The speed of image acquisition of this system is extraordinary relative to that of the spinning confocal system, which generally allows for the recording of ≤10 volumes/s. This dataset includes both large cells with stronger intensities that are easy to segment and small cells with weaker intensities that are difficult to segment (Figure 7B, top and middle). The photobleaching in the dataset made it challenging to detect and track the weak intensity cells in the later part of the imaging, because of the substantial overlap between the small cell signals and background signals (Figure 7B; Figure 7—figure supplement 1). This weak intensity is unavoidable because of the extremely quick scanning rate of the system (10,568 frames per second). In addition, the rapid beating of the heart caused relatively large movements of all the cells in the x-y-z directions (Figure 7B, bottom; Figure 7—video 1; see below), making cell tracking more challenging than that for worm #1–3, which predominantly moved in the x-y plane.

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After segmentation, we manually confirmed 98 cells in the first volume and tracked them automatically (Figure 7C and D; Figure 7—video 2). We found that among the 30 larger cells (size: 157–836 voxels) with higher intensities, 26 of them (87%) were correctly tracked in all 1000 volumes. Even though the smaller cells with lower intensities were more difficult to track, when including them, we still correctly tracked 66 out of 98 cells (67%) (Figure 7E and F). The tracked movements of two example cells showed regular oscillations in 3D space (x, y, and z axes; Figure 7G), consistent with the regular beating movement of the heart. It should be noted that we did not optimize the pipeline procedures for the zebrafish data, except for a few parameter changes (Table 2). Our results indicate that 3DeeCellTracker is also capable of analyzing images with rapid and dynamic movements in 3D space, obtained from a substantially different optical system.

We then used the tracking results to answer a biological question: What is the relationship between the intracellular calcium signals and the beating cycles of the heart? We extracted the calcium dynamics of the correctly tracked 66 heart cells, which co-express GECI as in the worm datasets, and analyzed the phases of activities in these cells relative to the beating cycles of the ventricular and atrial regions, respectively. As shown in Figure 7H and I, the calcium signals were largely synchronized with the beating cycles. Although a portion of the heart cells (32/98 total) was not correctly tracked and therefore not analyzed, this result is still remarkable because the tracked cells show the relationships between calcium dynamics and the natural heartbeats in vivo. Observation of this relationship was only made available by the developments of a state-of-the-art microscope system that can monitor 100 volumes per second and of our software pipeline that can correctly track large portions of the corresponding cell movements in 3D space.

We also tested our method on a dataset more related to biomedical application, namely a dataset of ~900 cells in a 3D multicellular tumor spheroid (MCTS) imaged with a two-photon microscope. 3D MCTS is increasingly being used for drug screening because of its similarity to tumor cells in vivo (Riedl et al., 2017). Therefore, the measurement of individual cell activities in 3D MCTS has become necessary, although tracking the movements of large numbers of cells in 3D + T images of 3D MCTS has presented a considerable challenge.

We obtained 3D + T images of a 3D MCTS cells expressing FRET type ERK sensor, EKAREV-NSL (Komatsu et al., 2011) using a two-photon microscope (see Materials and methods). This dataset shows normal distributions of intensities and movements but includes a much larger number of cells than either the worm brain or the zebrafish heart dataset (Figure 8A and B; Figure 8—video 1). Furthermore, cell division and death occurred during the imaging. Our method segmented and tracked 901 cells, of which we visually inspected the tracking results of 894 cells (the remaining seven cells were found to have segmentation errors in volume #1). We excluded the cells that experienced cell death or cell division after such events occurred, and found that 869 out of 894 cells (97%) were correctly tracked (Figure 8C–E, Figure 8—video 2). Using the tracking results, we extracted the ERK activity from the FRET signal. In three representative cells with cross-layer movements, we confirmed that the extracted signals correctly reflected intensity changes in the cells (Figure 8F). We also found that the spheroid as a whole moved downwards, although each cell moved in a different direction (Figure 8G).

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In the assessments described in the preceding sections, we successfully analyzed multiple types of datasets that differ in terms of image resolution, signal-to-noise ratio, and types of cell movement etc. We then systematically evaluated the performance of our method (single mode) under a variety of conditions using a series of degenerated datasets obtained by modifying the worm #3 dataset. For a fair comparison, we used the same pre-trained 3D U-Net and the same manually corrected segmentation at t = 1 used on the original worm #3 dataset.

A general difficulty in segmentation arises from images with low signal-to-noise ratios. Excessive noise can obscure the real cell signal, leading to incorrect segmentation and ultimately incorrect tracking. We tracked cells in three degenerated datasets with different levels of Poisson noise added to the original images: sd = 60, sd = 100, and sd = 140 (Figure 9A). The noise level in the original images in the non-cell regions was sd = 4.05 and the median intensity was 411, whereas the median intensity of cell regions was 567 with a 95% confidence interval of 430–934, indicating a tiny overlap between non-cell and cell regions (Figure 3—figure supplement 3B). In the sd = 60 condition, the intensities of non-cell and cell regions overlapped to a greater extent (Figure 9B), and image quality appeared poorer than that of the original image (Figure 9A). Nevertheless, our method achieved a low error rate (6/175 = 3%; Figure 9C–E). Even in the sd = 140 condition, in which the intensities overlapped extensively (Figure 9B) and the image quality was quite poor (Figure 9A), our method achieved an acceptable error rate (16/175 = 9%, that is, 91% correct; Figure 9C–E). Note that the tracking error rate was much lower than the segmentation error rate (16/175 vs 57/175, respectively; Figure 9—figure supplement 1), indicating that our FFN-based tracking method can compensate for errors in cell segmentation. Considering that the overlap of intensity distributions between cell regions and background in the degenerated datasets were much more severe than the overlap in the real datasets (Figure 9B and panel B in Figures 3, 6, 7 and 8), these results suggest that our method can robustly track cells in 3D + T images with severe noise.

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Another difficulty comes from large displacements of cells between volumes during tracking. We enhanced this effect by removing intermediate volumes in the worm's 3D + T images, and then tested the three datasets with 1/2, 1/3, and 1/5 of the volume of the original dataset (Figure 9F). As expected, when more volumes were removed, the movements of cells and the number of tracking errors increased (Figure 9G–L). Nevertheless, the error rate in the 1/3 vol condition was acceptable (14/175 = 8%, i.e. 92% correct), while the error rate in the 1/5 vol condition was relatively high (25/175 = 14%, i.e. 86% correct).

We then tested whether our method can track cell movements along the z-axis, in which the resolution is much lower than that in the x-y plane. In such conditions, 3D + T tracking is more challenging along the z-axis than in the x-y 2D plane. In the deforming worm and the tumor spheroid datasets, the resolution along the z-axis was approximately 1/10-1/5 of that in the x-y plane (panel A in Figure 3, Figure 3—figure supplements 1, 2 and 3, and Figure 8). We confirmed that 5, 16, 25, and 668 cells in worm #1a, #1b, and #3, and tumor spheroid datasets, respectively, showed cross-layer movements along the z-axis. Still, our method correctly tracked all of those cells. For example, while two cells in the worm #3 dataset exhibited multiple cross-layer movements (Figure 9—figure supplement 2), they were correctly tracked until the end the sequence. Furthermore, we also degenerated the zebrafish heart data to test whether our method can track dynamic 3D cell movements with unequal resolutions. Even when the zebrafish image resolution in the z-axis was reduced by sum binning with a shrink factor = 2, our method was able to correctly track a majority of the cells (80% of the 30 larger cells and 53% of all 98 cells; Table 3). Together, these results indicate that our method can correctly track cells in various conditions including severe noise, large movements and unequal resolution.

To evaluate the tracking performance of our method on datasets with large movements, we summarized the RM values, which are listed in Table 3 and Figure 10 (see also Figure 4). Although many cell movements in the worm #3 and the zebrafish datasets had RM ≥0.5, most of the worm neurons and the larger cells in the zebrafish heart were correctly tracked by the single mode. In addition, our program with the ensemble mode achieved 99.8% tracking of the neurons of a ‘straightened’ freely moving worm while many cell movements in this dataset had RM ≥1. These results indicate that our method is capable of analyzing images with challenging displacements.

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To investigate the relationships between the cell movements and tracking error rate, we drew a scatter plot of the averaged RM and corresponding error rate for each volume (Figure 10—figure supplement 1). The results indicated by the regression lines suggest positive correlations between the RM and error rate, although the correlation trends appear to differ by dataset, implying that movement is not the only factor affecting the error rate. We also drew a scatter plot and regression line for the worm #4 dataset tracked with the ensemble mode (Figure 10—figure supplement 2). The result suggests that the error rate is not correlated with movement, perhaps because in ensemble mode the cell positions are predicted from multiple different volumes, and therefore movement from a previous volume is not closely connected with the error rate.

To further assess the capabilities of our method, we compared the tracking performance of our method with that of two other state-of-the-art methods for cell tracking. The first was DeepCell 2.0, a newer version of DeepCell, which is a pioneer in segmenting and tracking cells in 2D + T images using deep learning (Bannon et al., 2018). Unlike our method, DeepCell 2.0 has been primarily tested on images that include cell divisions, birth and death, but not on images of deforming organs. The second was the software developed by Toyoshima et al., which does not use a deep learning technique, but has achieved a higher accuracy in both segmenting and tracking of worm whole brain datasets than any other previous methods (Toyoshima et al., 2016).

On our worm brain datasets, DeepCell 2.0 tracked ~10% of cells properly (Figure 11A, Figure 11—figure supplement 1A, Figure 11—video 1, and Table 4); this is probably because the tracking algorithm of DeepCell 2.0 is optimized for the movements associated with cell divisions, but not for quickly deforming organs. Toyoshima’s method tracked ~90% of the original worm #3 neurons but only ~10% of the degenerated ‘worm #3, 1/5 sampling’ (Figure 11B, Figure 11—figure supplement 1B, and Table 4). When tested on the zebrafish dataset, Toyoshima's method was able to detect only 76 cells, and missed some weak-intensity cells, performing worse than our method (detected 98 cells). Of the detected 76 cells, 21 were incorrectly tracked from the first volume, probably because their method automatically re-fits the cell shape using a Gaussian distribution after segmentation, which can lead to a failure when fitting weak-intensity cells. Their tracking accuracy was also lower than ours (Table 4). In addition, our method was found comparable to or more efficient in terms of runtime than DeepCell 2.0 and Toyoshima's methods (Table 5). These results suggest that our method is more capable of tracking cells in 3D + T images than previous methods.

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Our image processing task was performed on a personal computer with an Intel Core i7-6800K CPU @ 3.40 GHz x 12 processor, 16 GB of RAM, and an Ubuntu 16.04 LTS 64-bit operating system. We trained and implemented the neural networks with an NVIDIA GeForce GTX 1080 GPU (8 GB). The neural networks were constructed and implemented using the Keras high-level neural network API (https://keras.io) running on top of the TensorFlow machine-learning framework (Google, USA). All programs were implemented within a Python environment except for the image alignment, which was implemented in ImageJ (NIH; RRID:SCR_003070), and the manual labeling, manual correction and manual confirmation, which were implemented in ITK-SNAP (RRID:SCR_002010; http://www.itksnap.org) or IMARIS (Bitplane, UK; RRID:SCR_007370). Instead of ITK-SNAP and IMARIS, one can use napari (https://napari.org) in the Python environment.

Step 1: Because 2D images were taken successively along the z axis, rather than simultaneously, small or large displacements could exist between different layers of a 3D volume. Ideally, this should be compensated for before the segmentation procedure. Using the StackReg plugin (Thevenaz et al., 1998) in ImageJ (NIH), we compensated for the displacements by using rigid-body transformations to align each layer with the center layer in worm #1 and #2 datasets. However, we did not apply this alignment in worm #3, #4, zebrafish, and tumor spheroid datasets but still obtained acceptable results, indicating this step may be skipped.

Step 2: Cells in the same image could have very different intensities, and detecting weak cells is generally difficult. To solve this problem, we applied local contrast normalization (Goodfellow et al., 2017) through a sliding window (27 × 27 × 3 voxels) so that all cells had similar intensities. This normalization was applied to the nucleus marker images only for tracking and did not affect the calculation of the signal intensities for either red fluorescent protein or GECI.

We used 3D U-Net structures similar to that shown in the original study (Çiçek et al., 2016). The network received a 3D image as input and generated a 3D image of the same size with values between 0 and 1 for each voxel, indicating the probability that the voxel belonged to a cell region (Figure 2—figure supplement 1). We used different structures of 3D U-Net for different imaging conditions, in order to capture as much information as possible of each cell within the limitations of the GPU memory. Such modification of the U-Net structures is preferred but not necessary. The same structure can be reused on different datasets because of the flexibility of deep learning methods. The 3D U-Net structure shown in Figure 2—figure supplement 1A was used on our datasets worm #1 and worm #2, which have identical resolution, but we also successfully used the same structure in the binned zebrafish dataset (see below), which had very different resolution. For dataset worm #3, which had a lower resolution, we reduced the number of maxpooling and upsampling operations so that each voxel in the lowest layers corresponds to similar sizes as in the datasets worm #1 and worm #2. We also reduced the sizes of the input, output, and intermediate layers because of the lower resolution. As the smaller structure occupied less GPU memory, this allowed us to increase the number of convolutional filters on each layer so that the capacity of the network was increased (see Figure 2—figure supplement 1B). For the zebrafish dataset, although it has even lower resolution in the x and y axes, because the sizes of zebrafish cardiac cells are larger than worm neurons, we used the same number of maxpooling and upsampling operations as in datasets worm #1 and worm #2. We adjusted the sizes of layers in the x, y, and z dimensions to a unified value (=64) because the resolution in the three dimensions are not very different in the zebrafish dataset (Figure 2—figure supplement 1C). For simplicity, we reused the structures A and B in Figure 2—figure supplement 1 for worm #4, tumor spheroid and binned zebrafish dataset (see Table 2).

The U-Net can be trained using very few annotated images (Ronneberger et al., 2015). In this study, we trained six 3D U-Nets: (1) for datasets worm #1 and #2, (2) for dataset worm #3, (3) for freely moving dataset worm #4, (4) for the zebrafish dataset, (5) for the dataset tumor spheroid, and (6) for the binned zebrafish dataset. Each 3D U-Net used one 3D image for training. Note that, although datasets worm #1 and #2 are substantially different with respect to signal intensity and cell movements, the same trained 3D U-Net was used. The image was manually annotated into cell regions and non-cell regions using the ITK-SNAP software (http://www.itksnap.org). We used the binary cross-entropy as the loss function to train the 3D U-Net. Because the raw image sizes were too large (512 × 1024 × 28, 256 × 512 × 20, 180 × 260 × 165, etc.) for computing in the GPU, we divided the raw images into small sub-images that fit the input sizes of the three 3D U-Nets structures (160 × 160 × 16, 96 × 96 × 8, or 64 × 64 × 64), and combined the cell/non-cell classifications of sub-images to form a final classification of the whole image. To improve the 3D U-Net performance, we increased the training data by data augmentation: We applied random affine transformations to the annotated 3D images by ‘ImageDataGenerator’ class in Keras. The affine transformation was restricted in the x-y plane but not in the z-direction because the resolution in the z-direction is much lower than that in the x-y plane for worm datasets and the tumor spheroid dataset (see panel A in Figure 3, Figure 3—figure supplements 1, 2 and 3, and Figure 8). Although the zebrafish dataset has similar resolutions in the x, y, and z directions, we applied the same affine transformation for simplicity. We trained the U-net for datasets worm #1 and #2 using a 3D image from another dataset independent of #1 and #2 but obtained under the same optical conditions, and its classifications on datasets worm #1 and #2 were still good (panel C in Figure 3, Figure 3—figure supplements 1 and 2), indicating a superior generalization ability of the 3D U-net. Because only one dataset is available for the specific resolutions of dataset worm #3, #4, the zebrafish heart, and the tumor spheroid, we trained the 3D U-Net by using the first volume of 3D + T images of each dataset and then applied the 3D U-Net to all the following 3D images of the datasets.

The 3D U-Net generated probability outputs between 0 and 1, which indicated the probability that a voxel belonged to a cell-like region. By setting the threshold to 0.5, we divided the 3D image into cell-like regions (>0.5) and non-cell regions (≤0.5). The cell-like regions in the binary images were further transformed into distance maps, where each value indicated the distance from the current voxel to the nearest non-cell region voxel. We applied a Gaussian blur to the distance map to smooth it, and searched for local peaks which were assumed to be cell centers. We then applied watershed segmentation (Beucher and Meyer, 1993), using these centers as seeds. Watershed segmentation was applied twice; the first application was 2D watershed segmentation for each x-y plane, and the second application was 3D watershed segmentation for the entire 3D space. Two segmentations were required because the resolutions in the x-y plane and the z-dimension differed.

An initial matching, that is, a set of correspondences between cells in two temporally adjacent volumes, is the first step for cell tracking in our pipeline and critical for the final tracking accuracy. The correspondences can be estimated based on the relative cell positions, assuming that these positions do not substantially change even during organ deformation. By comparing the similarity between the relative positions of two cells in different volumes, we can determine whether they are the same cells.

One conventional method to represent relative positions is fast point feature histograms (FPFH) (Rusu et al., 2009). The PR-GLS study (Ma et al., 2016) successfully used the FPFH method to match artificial point set datasets. However, we found that FPFH yielded a poor initial match for the datasets considered in this study (Figure 2—figure supplement 3A), perhaps because of the sparse distribution of the cells. We thus designed a three-layer feedforward network (FFN) to improve the initial match (Figure 2B). The three-layer structure generated good match results comparable with those of more complex structures with four or six layers. By comparing the representations between two points, the network generated a similarity score between two cells. The initial matching based on the similarity score by the FFN was more accurate than that achieved by the FPFH method (Figure 2—figure supplement 3B). Our FFN + PR-GLS approach generated more accurate predictions of cell positions than FPFH + PR-GLS and the simple method of affine alignment (using an implementation of the Coherent Point Drift Algorithm: https://github.com/siavashk/pycpd) (Figure 2—figure supplement 3C and D).

In our FFN, the input of the network contained the position information of two points. Each point was represented by the normalized positions of the 20 nearest neighboring points (Figure 2B). The 20 nearest neighbor positions (${\displaystyle \overrightarrow{{\mathbf{d}}_1},\overrightarrow{{\mathbf{d}}_2},\dots ,\overrightarrow{{\mathbf{d}}_{20}}}$) were given by 3D vectors because they were extracted from the 3D image. To normalize the points, each of the 20 positions was divided by the mean distance d ($\mathrm{d}=\frac{1}{20}\sum _{\mathbf{k}=1}^{20}\left|\overrightarrow{{\mathbf{d}}_{\mathbf{k}}}\right|$). The normalized positions were then sorted by their absolute values in ascending order. Finally, the mean distance d was included as the last value, so each point was represented by a 61D vector.

We utilized the first fully connected layer to calculate the learned representation of the relative positions of each point as a 512D vector (Figure 2B, the first hidden layer after the input). We then applied a second fully connected layer on these two 512D vectors to compare the representations of the two points. The resulting 512D vectors (the second hidden layer after the input) were processed by a third fully connected layer to obtain a single similarity score between 0 and 1, which indicated the probability of two points originating from the same cell. We matched the two points with the highest scores in two different volumes, ignored these two points, and matched the next set of two points with the highest scores. By repeating this process, we obtained an initial match (Figure 2C, panel 4–1).

In this study, we only trained one FFN based on one original image of dataset worm #3 and used the network in all the datasets including worms, zebrafish and tumor spheroid. To train the network, we first performed segmentation on a single volume of dataset worm #3 and obtained a point set for the centers of all cells. Because we required a large number of matched point sets for training, and because manually matching point sets is time-consuming and impractical, we created an synthetic training dataset by applying random affine transformations to the point set described above and adding small random movements to each point according to following equations:

Also see Figure 2—figure supplement 2A for an illustration. Here $\overrightarrow{x}$ is the mean-removed 3D position {$x_1$, $x_2$, $x_3$} of each point in the original point set, while $\overrightarrow{x^{\text{'}}}$ is the transformed 3D position. $A$ is a matrix to apply the random affine transformation. More specifically, $A=I+U$, where $I$ is a 3 x 3 identity matrix, and $U$ is a 3 x 3 random matrix with each element $U_{ij}$ from a uniform distribution. We used $U_{ij}~uniform\left(-0.05,0.05\right)$ in this study. ${\displaystyle \overrightarrow{{\epsilon }_1}}$ is the 3D vector for adding random movements to each point in a point set, while ${\displaystyle \overrightarrow{{\epsilon }_2}}$ is for adding even larger random movements in a subset of points (20 out of 175 cells) to simulate serious errors from segmentation procedures. We used ${\epsilon }_{1,i}~uniform\left(-2,2\right)$ and ${\epsilon }_{2,i}~uniform\left(-5,5\right)$ in this study. By randomly generating $U_{ij}$, ${\epsilon }_{1,i}$ and ${\epsilon }_{2,i}$, we could generate an arbitrarily large number of new point sets with new positions from the original point set. After that, we chose a specific point A and another point B from each generated point set and the original point set, respectively, and we calculated their relative positions as inputs for the FFN. In half of the cases, points A and B are corresponding, that is, they come from the same cell, while in the other half, point A is from a cell adjacent to the cell of point B, thus they are not corresponding. In this study, we used 576,000 newly generated pairs of points A and B for training the FFN (Figure 1C). We used binary cross-entropy as the loss function to train the FFN. During the training, the performance of matching by FFN was gradually improved using an independent test dataset of two point sets (Figure 2—figure supplement 2B).

The initial match calculated using FFN was corrected using the expectation–maximization (EM) algorithm in the PR-GLS method, as described in the original paper (Ma et al., 2016). In the original study, the initial match (by FPFH) was recalculated during the EM iterations; however, in most datasets, we calculated the initial match only once (by the FFN) before the EM steps were performed, which did not cause problems. Only for the dataset worm #4 with very large movements, we recalculated initial matching by FFN after every 10 iterations, in order to improve accuracy. After the PR-GLS corrections, we obtained coherent transformations from the points of each volume to the subsequent volume (Figure 2C, panel 4–2).

The FFN + PR-GLS can predict new cell positions at time t from the cell positions at t-1; this is the default mode of our pipeline, which is referred to as the single mode (Figure 6C). At a sufficiently high sampling rate, the single mode is reasonable because the movement from t-1 to t is much smaller than that from t-i (i > 1) to t, making the prediction from t-1 more reliable.

When cell movements are very large (such as in a freely moving worm), and the sampling rate is not sufficient, the prediction from t-1 to t becomes less reliable, and the average of multiple predictions from t-i to t may be more accurate. Therefore, we developed an approach using the average of multiple predictions, referred to as the ensemble mode (Figure 6C). We tested this mode only on the worm #4 dataset, which had quite large movements (Figures 6B and 10, and Table 3), because the runtime of the ensemble mode is much longer than that of the single mode, that is, the runtime of the FFN + PR-GLS component is proportional to the number of predictions used. Specifically, the runtime including segmentation and tracking for worm #4 was approximately 30 volume/h in the single mode and 6 volumes/h in the ensemble mode.

In the ensemble mode, we calculated an average of up to 20 predictions from previous time points. In cases for which t ≤ 20, the averages was calculated from time points [t-1, t-2, …, 1]; in cases for which t > 20, it was calculated over [t-d, t-2d, …, t-20d], where d is the quotient (t-1)//20.

By applying the PR-GLS method to the initial match, we obtained a more reliable transformation function in which all obvious incorrect matches were corrected. However, small differences still existed in a few cells, which could accumulate over time to become large differences without correction. Thus, we included an additional automatic correction step, in which the center position of each cell was moved slightly toward the centers of each 3D U-Net detected region (for details, see Figure 2—figure supplement 4). After correction, all cells were moved to the estimated positions with their shapes unchanged from volume #1. For most of the datasets, we applied only one correction; for the worm #4 and tumor spheroid datasets, we applied corrections up to 20 times, until achieving convergence. If multiple cells overlapped in the new positions, we applied the watershed method again to assign their boundaries. In carrying out this step, we calculated the images of the tracked cell regions based on an interpolation of the raw image from volume #1, when the resolution along the z axis was much lower than that in the x-y plane (i.e. all datasets except for the worm #4 and the zebrafish). We did this in order to obtain more accurate estimates for each cell region.

We manually corrected the segmentation only in volume #1. We superimposed the automatically segmented regions of the volume #1 3D image onto the raw 3D image in the ITK-SNAP software, and we discarded false positive regions, such as autofluorescence regions and neuronal processes. If any cells were not detected (false negative error), we reduced the noise level parameter in the pre-processing (Table 1) which can eliminate such errors, or we manually added these cells in ITK-SNAP. Oversegmentation and undersegmentation regions were corrected carefully by considering the sizes and shapes of the majority of cells. The overall error rates depend on the image quality, but we usually found that around 10% of cells required manual correction which usually took 2–3 hr (for 100–200 cells).

We counted the tracking errors of all cells by visually inspecting the tracking results in each volume (Figure 1—figure supplement 2). To confirm the tracking of worm’s neurons, we combined two 3D + T images—the raw images and the tracked labels—in a top–bottom arrangement displayed as a hyperstack by the ImageJ software to compare the cell locations in each volume. As the cells in worms’ datasets primarily moved in the x-y plane (Figures 3F and 9L, and Figure 3—figure supplement 2F), we observed the correspondence between the raw images and the tracked labels in each x-y plane to identify tracking errors in the results. To confirm our tracking of the tumor spheroid dataset, we applied the same method although there were many small cross-layer movements in the images (Figure 8F and G).

It was more difficult to confirm the tracking results in the hyperstack images for the cells in the freely moving worm and the zebrafish heart than in the semi-immobilized worm and tumor spheroid, due to the frequent occurrence of large movements of cells across layers (Figures 6G and 7G). Therefore, we superimposed images of the tracked labels onto the raw images and imported them into IMARIS (Bitplane, UK), and then visually checked the tracking results of each cell individually in 3D mode. For the zebrafish dataset with repeated oscillations, we tracked and checked all 98 cells from 1000 volumes. Because the freely moving worm engaged in irregular movements in three directions, visual checking was more challenging, and thus we only tracked and checked the initial 500 volumes of 3D images out of 1519 original volumes (Figure 6F).

Large movements of cells is one issue that makes tracking challenging. To evaluate how challenging each cell movement is, we defined the 'relative movement' (RM) of cell A at time t as: RM = (movement of cell A from t-1 to t) / (distance between cell A and its closest neighboring cell at t).

Figure 4A middle and right panels illustrate two cells moving in one dimensional space with RM ≥0.5. In this condition, a very simple tracking method, ‘search for the closest cell at the next time point’ will mistakenly match the cell A at t = 2 to the cell B at t = 1. Therefore, we argue that movements with RM ≥0.5 are more challenging than movements with RM <0.5.

It should be noted that large movement is not the only challenge for tracking. For example, the zebrafish datasets have much higher error rates if we evaluate the tracking in all cells (Table 3), which is likely caused by the weak intensities in these small cells and the photobleaching (Figure 7B and F; Figure 7—figure supplement 1).

After having tracked the worm’s neurons from the first volume to the last volume, we extracted activities from the regions corresponding to each neuron. By measuring the mean intensities of each neuron in both channels corresponding to the GECI and the positional markers, the activity was computed as GCaMP5G/tdTomato in dataset worm #1. We similarly extracted calcium activities (GCaMP) of heart cells in the zebrafish dataset and the FRET activity in the tumor spheroid dataset.

Because DeepCell 2.0 currently only supports tracking cells in 2D datasets, we tested it using two layers of images (z = 9 and z = 16) from worm dataset #3, which include relatively large numbers of cells. We excluded cells that disappeared or appeared due to cross-layer movements for a fair comparison. We supplied DeepCell 2.0 with the precise segmentations from our method in order to focus on comparing the performance of the tracking algorithms.

We tested Toyoshima’s software using two worm datasets and one zebrafish dataset. For the zebrafish dataset, we only tested the initial 100 volumes because their method required a longer time for the imaging processing than ours did (see Table 5).

For these two methods, we communicated with the corresponding authors and did our best to optimize the parameters based on their suggestions. Nevertheless, it is possible that those analyses could be further optimized for our datasets.

We tested the runtimes of our method and the previous methods for different datasets (see Table 5). Because DeepCell 2.0 currently can only track 2D + T images, the runtime was estimated by using the average runtime for one layer and multiplying that by 21 layers, in order to compare it with our method. As a result, DeepCell 2.0 required a runtime comparable with our method. On the other hand, Toyoshima’s software took a much longer time than our method to process the worm and zebrafish datasets.

In our method, the initial matching using our custom feedforward network is performed in a pairwise fashion, so the time complexity is O(n²), where n is the number of detected cells. In our tested datasets with 100 ~ 200 cells, this did not take a long time, for example,~3.3 s were required for matching 98 cells between two volumes (zebrafish), or ~8.6 s for 164 cells (worm #1a) using our desktop PC. In cases where the n is too large, the runtime may become much longer, for example,~4 min for 901 cells (tumor spheroid). If both n and volume number are too large, it may be required to optimize the method to reduce the runtime, for example, by restricting the calculation of matchings in a set (100 ~ 200 or more) of representative cells (e.g. large/bright cells), while movements of other non-representative cells can be estimated from the movements of these representative cells utilizing the coherency of these movements in a deforming organ.

The techniques used to culture and handle C. elegans were essentially the same as those described previously (Brenner, 1974). Both TQ1101 lite-1(xu7) and AML14 were obtained from the Caenorhabditis Genetics Center (University of Minnesota, USA). Young adult hermaphrodites were used in the imaging experiments.

For pan-neuronal expression, NLS::tdTomato::NLS (Frøkjær-Jensen et al., 2016) and NLS::GCaMP5G::NLS (in which GCaMP5G (Akerboom et al., 2012) was codon-optimized for C. elegans and attached to NLS at N- and C-termini) were fused with the rab-3 promoter (Stefanakis et al., 2015) using a GATEWAY system (Thermo Fisher Scientific). Germline transformation into lite-1(xu7) (Liu et al., 2010) was performed using microinjection (Mello et al., 1992) with a solution containing pYFU251 rab-3p::NLS::GCaMP5G::NLS (25 ng/µl), pYFU258 rab-3p::NLS::tdTomato::NLS (20 ng/µl) and OP50 genome (55 ng/µl) to obtain the strain KDK54165. The strain lite-1(xu7) was used to reduce the blue light-induced activation of the worm’s sensory system (Liu et al., 2010). Independent transgenic lines obtained from the injection produced similar results.

In this study, we used four worm datasets. The 3D images in datasets worm #1 and #2 were obtained using our custom-made microscope system, OSB3D (see below). The worm strains for datasets worm #1 and #2 are KDK54165 (RRID:WB-STRAIN:KDK54165) and AML14 wtfEx4[rab-3p::NLS::GCaMP6s: rab-3p::NLS::tagRFP] (RRID:WB-STRAIN:AML14) (Nguyen et al., 2016), respectively. The 3D + T images of the dataset worm #3 were published previously with the worm strain JN2101 Is[H20p::NLS4::mCherry]; Ex[tax-4p::NLS-YC2.60, lin-44p::GFP] (Toyoshima et al., 2016). The 3D + T images of the freely moving worm dataset (worm #4) were published previously with the worm strain AML14 (Nguyen et al., 2016; Nguyen et al., 2017). In three (#79, #135, and #406) out of the 500 volumes of the worm #4 dataset, most of the cells disappeared from the volume or a large amount of noise occurred, which rendered the dataset impossible to analyze. These volumes were therefore manually skipped for tracking, that is the cells were assumed not moved from the previous volumes.

We upgraded our robotic microscope system (Tanimoto et al., 2017) to a 3D version. We used a custom-made microscope system that integrated the Nikon Eclipse Ti-U inverted microscope system with an LV Focusing Module and a FN1 Epi-fl attachment (Flovel, Japan). The excitation light was a 488 nm laser from OBIS 488–60 LS (Coherent) that was introduced into a confocal unit with a filter wheel controller (CSU-X1 and CSU-X1CU, respectively; Yokogawa, Japan) to increase the rotation speed to 5,000 rpm. The CSU-X1 was equipped with a dichroic mirror (Di01-T-405/488/561, Semrock) to reflect the 488 nm light to an objective lens (CFI S Fluor 40X Oil, Nikon, Japan), which transmitted the GCaMP fluorescence used for calcium imaging and the red fluorescence used for cell positional markers. The laser power was set to 60 mW (100%). The fluorescence was introduced through the CSU-X1 into an image splitting optic (W-VIEW GEMINI, Hamamatsu, Japan) with a dichroic mirror (FF560-FDi01, Opto-line, Japan) and two bandpass filters (BA495-540 and BA570-625HQ, Olympus, Japan). The two fluorescent images were captured side-by-side on an sCMOS camera (ORCA Flash 4.0v3, Hamamatsu, Japan), which was controlled by a Precision T5810 (Dell) computer with 128 GB RAM using the HCImage Live software (Hamamatsu) for Windows 10 Pro. A series of images for one experiment (approximately 1–4 min) required approximately 4–15 GB of space, and were stored in memory during the experiment, then transferred to a 1-TB USB 3.0 external solid-state drive (TS1TESD400K, Transcend, Taiwan) for further processing.

For 3D imaging, the z-position of the objective lens was regulated by a piezo objective positioner (P-721) with a piezo controller (E665) and the PIMikroMove software (PI, Germany). The timings of the piezo movement and the image capture were regulated by synchronized external edge triggers from an Arduino Uno (Arduino, Italy) using 35 ms intervals for each step, in which the image capture was 29.9 ms. For each step, the piezo moved 1.5 µm, and one cycle consisted of 29 steps. We discarded the top-most step because it frequently deviated from the correct position, and we used the remaining 28 steps. Note that one 3D image was 42 µm in length along the z-axis, which was determined based on the typical diameters of neuronal cell bodies (2–3 µm) and of a young adult worm’s body (30–40 µm). Each cycle required 1015 ms; thus, one 3D image was obtained per second. This condition was reasonable for monitoring neuronal activities because the worm’s neurons do not generate action potentials (Goodman et al., 1998) and because many neuronal responses change on the order of seconds (Nichols et al., 2017). We also tested a condition using 10 ms for each step and 4.9 ms for an exposure with the same step size and step number per cycle (i.e. 2.3 volumes of 3D images per second), which yielded a comparable result. For cyclic regulation of the piezo position, we used a sawtooth wave instead of a triangle wave to assign positional information because the sawtooth wave produced more accurate z positions with less variance between cycles.

Sample preparation and imaging have been described previously (Voleti et al., 2019). In brief, GCaMP and dsRed were expressed in the cytosol and the nuclei of myocardial cells, respectively. The 3D + T images obtained with the SCAPE 2.0 system were skew corrected to account for the oblique imaging geometry and analyzed with 3DeeCellTracker. The 3D coordinates obtained from this study were used to extract calcium dynamics in the cells in the previous study (Voleti et al., 2019).

The tumor spheroid was cultured according to the previous procedures (Vinci et al., 2012; Yamaguchi et al., 2021). In brief, a suspension of HeLa cells (RRID:CVCL_0030, RCB0007, Riken Cell Bank; certified as mycoplasma-free and authenticated by using STR profiling) expressing the FRET-type ERK sensor EKAREV-NLS (Komatsu et al., 2011) was added to a PrimeSurface 96 well plate (MS-9096U, Sumitomo Bakelite) for 3D cell culture at a density of 1200 cells/well. The grown spheroids were transferred to 35 mm-dishes coated with poly-L-lysine (Sigma-Aldrich) and further grown with DMEM/F-12, no phenol red (ThermoFisher) containing 10% FBS and 1% penicillin and streptomycin.

The 3D + T images of the spheroid were recorded using a two-photon microscope equipped with a water-immersion objective lens (N25X-APO-MP 25x CFI APO LWD objective, Nikon) and a high-sensitivity gallium arsenide phosphide (GaAsP) detector (A1R-MP+, Nikon). An 820 nm optical pulse generated by a Ti:Sapphire laser was used for the excitation. The fluorescence was split using two dichroic mirrors (FF495-Di03 and FF593-Di02, Opto-line), and the split lights below 495 nm and between 495–593 nm were detected by independent channels (CH1 and CH2, respectively). The fluorescence signals were integrated four times to increase the signal-to-noise ratio. Each step size was 4 µm along the z-axis, and each volume comprised of 54 steps, requiring 5 min for recording. Cells that experienced cell death or cell division were excluded manually after the events when evaluating the tracking accuracy.

The code for tracking cells and for training neural networks, the demo data, the pre-trained weights of the neural networks, and instructions for installation and use of the code are available in http://ssbd.qbic.riken.jp/set/20190602/ (Demos190610.zip). An updated version of the code can be found in https://github.com/WenChentao/3DeeCellTracker. The guides for using the codes and for setting parameters have also been included in the same GitHub repository.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

We thank Nobutoshi Odajima (Flovel Co. Ltd.), Hideki Tanaka (Yokogawa Corp.), Kenichi Matsumoto (Hamamatsu Photonics KK), and Kazuma Etani (Nikon Solutions Co. Ltd.) for setting up the OSB-3D. We also thank William Graf and David Van Valen for their kind advice in testing DeepCell 2.0, and Yu Toyoshima for his kind advice and help in testing the software by Toyoshima et al., 2016. We also thank Toru Tamaki, Ichiro Takeuchi, Takuto Sakuma, Katsuyoshi Matsushita, Hiroyuki Kaneko, Taro Sakurai, Jared Young, Richard Yan, Yuto Endo and the other Kimura laboratory members for their valuable advice, comments and technical assistance for this study. Nematode strains were provided by the Caenorhabditis Genetics Center (funded by the NIH Office of Research Infrastructure Programs P40 OD010440). Zebrafish samples were provided by Kimara Targoff, Caitlin Ford and Carmen de Sena Tomás and imaged with assistance from Citlali Perez-Campos and Wenze Li.

© 2021, Wen et al.

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