- August 14, 2020

Commentary: Tracking Biological Cells in Time-Lapse Microscopy Sijie Wen Z5185708 Introduction In this paper, the author proposed a new method to solve the difficulty in detection and tracking cells in time-lapse microscopy. In the field of cellular structure researching, tracking cells to see how they change with time is an important method in researching, since they could clearly see how the cellular structure changes under certain conditions and discover new findings. This leads to the development of time-lapse microscopy; this is the tool to record the behaviour of cells over time. However, even with such tool, it is still challenging for scientists to track down cells, because this means that they have to record the whole process and track cells in real-time and also have to stay around the microscopy to make sure that they did not lose track of cells, this is a huge workload for scientists and could make mistakes easily. This leads to the research of automated cellular structure tracking, by using this technology, scientists could be relieved from the tedious work of detection and tracking. Nonetheless, this technology is not perfect for now, it may wrongly detect cells because of overlapping or mitosis and many other unexpected reasons, thus the algorithm would lose track of cells and lead to cumulative error. Previous methods did not solve these problems perfectly, in this paper, the author proposed a pipeline model that uses an adaptive technique and topological features to improve the performance of detection and tracking, they also tested it on the datasets to shows their improvement compare to the previous method. Methods In this part, we will give a brief discussion about the proposed model which includes his basic theory. Additionally, we also simply discuss some possible improvements that can be done in this paper. The detection model used in this paper is based on the morphological top-hat filter [1] to correct illumination error and h-maxima transformation [2] to split cells. Because in a real scenario, a cell is a dark region surrounded by a bright halo. This leads to low contrast between the cell and its background thus making it hard to detect. In order to solve this problem, the paper used a top hat filter to correct the illumination error around the cell and remove the shading artifact so that the cell is clearly represented. After removing the illumination of cells, we need to segment these cells, because different cells may have overlap or under the process of mitosis. To detect, the author discovered that the cell center is slightly brighter than its boundary, thus can transform this problem into a simple form, which we just need to suppress the local maximum which corresponds to the boundary. To do this, the paper first used a Gaussian filter to generate a unique intensity maximum inside each cell and also remove some noise, because noisy data might influence the detection result, these preprocessing steps could benefit the finding of the cell center. After the filter, the author used an h-maxima transformation to suppress local maxima and find regional maximum, where h is a parameter computed based on several feature parameters of the cell. In order to reduce the effect of irregular boundaries of cells, they also fit an ellipse to the result of every detection, where the ellipse is found by using the Least Square method. H-maxima transformation used in here is a very nice choice because it originates from the feature that found by the author and is a simple and clear method to solve this problem efficiently, additionally, in the original edition, the h is a hyper parameter that is set by trial, however, in this paper, the author proposed a new method the adaptively calculate the h value. Another important aspect would be tracking, in order to track, you need to identify whether a cell in a consecutive frame is the same cell. In this paper, they introduced several topology features for cell, like displacement, skewness area, and so on. They use these features to describe certain properties of a cell thus could help in tracking cells since these properties may not change drastically over time. Then they use these features to match cells, they did it in a graph way, they compute the match cost of different cells thus having graph-like data, where the data of the table represents the cost of a cell matching another cell. They take this as a bipartite graph, then they use the Hungarian algorithm [3] to find the best match. After the successful match, we also need to recover the trajectory of cells, because cells might leave, enter, or having mitosis, thus the trajectory might be broken from this frame to the next frame for a single cell. To recover the trajectory, the author firstly assigns IDs to different cells and categorise them into 3 types based on the appearance of ID on the previous or consecutive frame. Then they generate an elliptical matching template which is used to convolve with frames, then it could classify the broken trajectory into 3 types that they described above, then they could recover the trajectory based on the different broken trajectory types. For the algorithm used here, I do not think this is a good option. Because it just uses a template to match and find results based on it, which is very rigid. Unlike many other things, the structure of cells is evolving with time, therefore the trajectory of cells is also related with time, which means previous location influences future location, however, this important factor is not considered in this part, since a template matching method has no consideration between the relationship of different frames. In this part, I think that the Bayesian model could perform better because it can relate future location and previous location through conditional probability rather than a simple template matching. Results The result of this paper can be concluded into 2 parts, first is the detection experiment, second is the trajectory experiment and the performance-related with the number of cells. In the first part, the author tests the proposed model on the detection. From the result, the proposed model could obviously detect cells better than other algorithms. We could see that it can generate more obvious boundaries than other methods and thus having a clearer segmentation of cellular structure. However, even the result seems nice, this experiment has some problems, one of the problems in the experiment is that the baseline method is too weak. The baseline used in this detection experiment is only a simple threshold method, which did not use any advanced algorithms in edge detection like Soble or Laplacian operator [4]. Outperform such easy baseline is not a surprising result, in order to convince the reader, it needs to be compared with other complicated methods to show the improvement, at least comparing to such a simple baseline would not persuade me to use this model in cell detection. Another problem is that it did not give any quantitative result in the cell detection, it only shows the qualitative test example in this experiment, however, a quantitative result could validate the author’s argument even more, since this is a detection task, which gives an accuracy table is not hard. However, such a result is not given in this part, which makes this experiment results in less convincing. The second experiment is tracking, it tracks 4 cells in this experiment for consecutive 700 frames, we could see the performance of tracking 4 cells is promising which reached 83% accuracy and cells after mitosis is also detected and tracked, this is mainly due to the template matching method. It uses both qualitative and quantitative measurement, for quantitative, it gives the tracking accuracy that is calculated based on the observed segments and valid segments. However, one fatal flaw here is that it did not even compare to any other tracking method in this experiment, not even a baseline model was given in this part. It means we cannot tell whether this model performs well or not since we do not have any information about how other tracking models would perform in this experimental setting. The author also tests the model’s performance under a different number of cells, however, one fatal flaw is also about the choice of baseline, it mentions about the 2 baseline model, but it does not give even any description about different features of these baseline tracking models, thus we do not know whether these modes are an advanced model or old models. Another problem is that since it compares with these 2 baselines, but why it did not compare it in the tracking accuracy. This is a confusing problem that did not have a proper explanation in this paper. Additionally, another point is that in the accuracy table he gives, there is no column of tracking accuracy when a cell enters or leaves the view, however, in the paper, this is described as one of the major reason that a broken trajectory situation might happen, which makes me question whether this model could handle this problem. From the reasons that I concluded above, I will not use his model because of a lack of reasonable comparison between models and also the lack of proper experiments about another situation. Conclusions In this article, we first introduced the background of automated cell tracking and its importance to biology research, then we have discussed some major difficulties in this research area. After the introduction, we give a brief description of the paper’s proposed model, this model is in a pipeline manner that combines different technologies to address detection and tracking problems. In detection, it uses a top-hat filter and h- maxima transformation to segment the cells then an elliptical matching is applied to match cells. After detection, they use different topology features to identity cells in different frames in which the best match is optimised using the Hungarian algorithm. Then this paper turns to address the broken trajectory problem that might happen in tracking, which is due to mitosis and entering or leaving of cells, this paper used an elliptical template matching method to recover the trajectory from frames. We also had a simple analysis of the resulting experiment of this model, in the detection task, the qualitative result seems very promising which could generate more obvious cell boundaries, however, this paper did not give a quantitative result and the baseline is too weak. The second experiment is in tracking, in this part, the paper shows it can track cells with 83% accuracy, however, in this part, it did not test on the entering or leaving situation and also did not have a baseline model to compare with. Additionally, it tests accuracy on the different numbers of cells with 2 baseline models, but we know little about these 2 baselines thus we could not conclude that the proposed model is whether state- of-the-art or not. The strength of this model is that it captures the topology features of cells, which discovered many useful features to detect or track, the algorithm is also simple and clear. However, pipeline model could be a weakness, since a pipeline model is complicated than an end-to-end model if this model could be an end-to-end model, it could be much better, another point that could be improved is in tracking, a template matching is really not a good idea since it lacks relatedness between different frames, this can be addressed using conditional probability model like Bayesian[5] which the current step can be established based on previous steps using conditional probability. For automated tracking, there are still several areas that could be further researched. For example, in a frame, there might be different types of cells, it could also benefit the scientist if the algorithm could automatically classify cells in the tracking process. If we could add cell classification into automated tracking, it could also benefit scientists and help them to do better research. References (1).An Introduction to Morphological Image Processing by Edward R. Dougherty, ISBN 0-8194-0845-X (1992) (2)Soille, P., “Morphological Image Analysis: Principles and Applications” (Chapter 6), 2nd edition (2003), ISBN 3540429883. (3) Harold W. Kuhn, “The Hungarian Method for the assignment problem”, Naval Research Logistics Quarterly, 2: 83–97, 1955. Kuhn’s original publication. (4) Gilbarg, D and Trudinger, N. Elliptic partial differential equations of second order. Springer. 2001. ISBN 978-3540411604. (5) Gelman, Andrew; Carlin, John B.; Stern, Hal S.; Dunson, David B.; Vehtari, Aki; Rubin, Donald B. (2013). Bayesian Data Analysis, Third Edition. Chapman and Hall/ CRC. ISBN 978-1-4398-4095-5.