## 2 Previous Work  
A very broad outline of medical imaging is given by the recent survey [1]. In the specific area of cell tracking, two broad categories are described in the existing literature: tracking by detection and tracking by model evolution [2; 3]. Tracking by detection is a two-stage method. The first stage involves processing each frame of a cell video to identify individual cells, segmenting on the basis of gradient, intensity, textures, etc. The second stage involves applying an optimization strategy to determine cell correspondence from frame to frame [4]. There are numerous tracking-by-detection methods detailed in the literature. Most algorithms use some variation of intensity of the image. For example, Ref. [5] use an adaptive threshold of intensity, opti  
Figure 2: Simple thresholding can result in significant noise as seen in Frame 26 (top right). While _Cell Universe_ has no problem dealing with such levels of noise—even across various choices of threshold—most algorithms we tested against require a much cleaner preprocessed (“segmented”) image such as the one at the bottom right. Video courtesy of YouTube [[https://www.youtube.com/watch?v=UccyMSQeleE](https://www.youtube.com/watch?v=UccyMSQeleE)]([https://www.youtube.com/watch?v=UccyMSQeleE](https://www.youtube.com/watch?v=UccyMSQeleE)) (accessed on 1 December 2021). mized at each pixel via integer programming into a binary cell detection at that pixel [5]. Seeded watershed algorithms primarily use intensity and its gradient to isolate probable locations of cells [6], while a two-stage \"split and merge\" has recently been proposed [7]. Gradient-based edge-detection attempts to isolate the boundary between a cell and its surroundings [8]. Shift-invariant wavelet frame transformations can isolate cells by optimizing coupled minimum-cost flow tracking [9]. Most open-source, readily-available software falls within this paradigm, including CellTrack [10], CellCounter [11], and most prominently, CellProfiler [12; 13]. Tracking-by-detection methods can be computationally inexpensive, but many of them require the user to estimate the gating threshold [6; 14]. Some advancements in the tracking-by-detection paradigm directed at remedying these problems include SAMTRA [15], which attempts to automatically determine gating thresholds, and Lineage Mapper [16], which uses segmented masks to bypass dependency on any particular segmentation method. Some tracking-by-detection methods do not require the user to estimate the gating threshold (for example, [9]), but these methods typically have less robust noise-handling techniques [4; 15]. Many of the above methods are designed to work on a single image, and fail to leverage the extra information available in a sequence of video frames, where information in adjacent (or even distant) frames can add significant value to interpretation of the current frame. Tracking by model evolution involves simultaneously segmenting and tracking cells in each frame of a cell video; the results of each frame are used to initialize the analysis of the following frame. This paradigm more easily accommodates morphological features of cells or user information about cell behavior. For example, models may be designed to allow cell division but prevent cell fusion [17]. Most model evolution methods evolve the contours of the cells [18; 19; 20]. Other approaches rely on topology-constrained level set methods [2; 21]. Although our method falls squarely within the model evolution paradigm, we make use of ensemble simulation rather than contour tracking or similar methods. Ensemble simulation involves building a realistic computer simulation of some system, and then initializing an ensemble of simulations of various states of the real system, all within the observational uncertainty of the real system. The simulations are then propagated, following approximately the same path as the real system for some nontrivial duration.