## 4 Data
The main aim of [87] is to evaluate and compare the performance of biomedical image segmentation made by trained experts, non-experts, and automated segmentation algorithms. Therefore, for each image, only one cell is annotated and provided as binary masks obtained in those three different ways. The gold standard annotation is obtained by majority voting of annotations created by the ten trained experts. **CTC** (Cell Tracking Challenge) is a time-lapse cell segmentation and tracking benchmark on publicly available data, launched in 2012 to objectively compare and evaluate state-of-the-art whole-cell and nucleus segmentation and tracking methods [22; 89]. The datasets consist of 2D and 3D time-lapse video sequences of fluorescent counterstainedan nuclei or cells moving on top or immersed in a substrate, along with 2D PhC and DIC microscopy videos of cells moving on a flat substrate. The videos cover a wide range of cell types and quality (spatial and temporal resolution, noise levels, etc.). The ground truth consists of manually annotated cell masks (for segmentation) and cell markers interlinked between frames to form cell lineage trees (for tracking). **DeepCell** comes from the supporting material of [23]. It consists of a PhC image sequence of HeLa-S3 cells. Annotations for each image are given in terms of cell and nuclei segmentation masks. **EVICAN** (Expert VIsual Cell ANnotation) [88] includes partially annotated grayscale images of 30 different cell lines from multiple microscopes, contrast mechanisms, and magnifications. For each image, a subset of cells and nuclei is annotated and provided both as json annotation files and as binary masks. An example is shown in Figure 3a,b. To reduce the influence of unannotated cells on the background class, in their experiments, the authors pre-processed the dataset by blurring (with a Gaussian filter) the images but leaving unchanged the annotated instances. The pre-processed images are also provided with the dataset (see Figure 3c). **fastER**[25] includes PhC, BC, and synthetic Fluo images of three different cell lines. For each image, the annotations consist of binary masks that enclose the segmentation of most of the cells and just the centroid for the remaining cells. **LIVECell**[26] is a recently proposed large-scale, manually annotated, and expert-validated dataset of PhC images for benchmarking cell segmentation. It consists of over 5 thousand images, including over 1.6 million cells of seven cell types (human and mouse) having different cell morphologies and culture densities. Annotations are provided as json files. **Usiagci**[63] includes 37 PhC images of T98G cells. Annotations consist of indexed masks, with an index for each cell, followed in time (see Figure 4). Thus, these can be  
Figure 3: Example data from the EVICAN dataset [88]: (**a**) original image (ID 92_ACHN); (**b**) image with annotated cells (red) and nuclei (blue); (**c**) image where non-annotated areas have been blurred. used for both segmentation and tracking. A spreadsheet file is also enclosed, providing information from tracking and various features for each tracked cell. **C2C12-16**[54] was released as a large-scale time-lapse phase-contrast microscopy image dataset for the mitosis detection task at the first international contest on mitosis detection in phase-contrast microscopy image sequences, held with the workshop on computer vision for microscopy image analysis (CVMI) at CVPR 2019. It is an extension of the [PERSON] et al. dataset [91] with manual annotations of mitosis. The complete dataset contains 16 sequences with 1013 frames per sequence and a total of 7159 mitosis events within the images. **Cell Tracking with Mitosis Detection Challenge** (CTMC) is a benchmarked challenge that provides DIC images for 14 cell lines [90]. The data adds up to 86 live-cell imaging videos consisting of 152,584 frames in total.