## 4 Data
In addition to the images, the challenge grants bounding box-based detection and tracking ground truths for each cell line, in the form of csv files for each video, including, for each frame and each cell, the cell ID and its bounding box coordinates. Recently, the dataset has also been adopted for the CTMC-v1 Challenge at CVPR 2022 ([[https://motchallenge.net/data/CTMC-v1/](https://motchallenge.net/data/CTMC-v1/)]([https://motchallenge.net/data/CTMC-v1/](https://motchallenge.net/data/CTMC-v1/)), accessed on 30 August 2022). **DeepESC**[55] consists of 785 PhC hESC images subdivided according to six classes (cell clusters, debris, unattached cells, attached cells, dynamically blebbing cells, and app  
Figure 4: Example data from the Usigaci dataset [63]: (**a**) original image (20180101 ef002 xy0101.tif), (**b**) corresponding indexed mask, where each color indicates a different cell in all sequence images. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Name** & **[Ref]** & **Task** & **Content** & **Link** & **\# Imgs** & **\# Events** \\ \hline C2C12-16 & [54] & Mitosis Detection & DIC & [[https://www.tifi-qiu.org/mitosisdetection/download/](https://www.tifi-qiu.org/mitosisdetection/download/)]([https://www.tifi-qiu.org/mitosisdetection/download/](https://www.tifi-qiu.org/mitosisdetection/download/)) \({}^{1}\) & 16,208 & 7159 \\ CTMC & [90] & Mitosis Detection & DIC & [[https://ivc.icschol.utreas.edu/ctmc/](https://ivc.icschol.utreas.edu/ctmc/)]([https://ivc.icschol.utreas.edu/ctmc/](https://ivc.icschol.utreas.edu/ctmc/)) \({}^{1}\) & 80,389 & 1616 \\ DeepESC & [55] & Classification & PhC & [[https://www.vislab.ucr.edu/SOFTWARE/software.php](https://www.vislab.ucr.edu/SOFTWARE/software.php)]([https://www.vislab.ucr.edu/SOFTWARE/software.php](https://www.vislab.ucr.edu/SOFTWARE/software.php))\({}^{2}\) & 785 & NA \\ \hline \hline \end{tabular} \({}^{1}\) Unavailable at the time of writing. \({}^{2}\) Accessed on 30 August 2022.  
\end{table} 
Table 3: Details of annotated data for cellular event detection and classification in traditional label-free sequences: dataset name (Name); reference ([Ref]); application of the data (Task); type of microscopy data (Content); url (Link); number of annotated images (# imgs), and number of annotated events (# events). All links were accessed on 28 August 2022. The dataset by **[PERSON] et al. **[91] includes 48 PhC image sequences of mouse C2C12 cells under various treatments. Annotations consist of manually tagged centroids and state (e.g., newborn, divided, or mitotic) for 10% of the cells for all the sequences; only for one of the sequences, all the cells are manually annotated. The dataset is also provided with annotations automatically generated for all the cells using in-house software based on segmentation, mitosis detection, and association. Other annotated microscopy image sets can be downloaded from the Broad Bioimage Benchmark Collection (BBBC) [92]. It is a publicly available collection of microscopy images intended as a resource for testing and validating automated image-analysis algorithms. Being contributed by many different research groups and for various applications, annotations are provided in varying forms (e.g., cell counts, masks, outlines, or bounding boxes).