## 2 Literature
### Cell Segmentation
Pre-processing of the images consists of denoising with bilateral filtering, while post-processing of the resulting masks includes hole-filling and size filtering. The software made publicly available (see Section 3) is shown to be robust against common cell segmentation challenges but still suffers high cell densities and blurring. Compared with other state-of-the-art methods (e.g., U-Net [37], ilastik [38], CellProfiler [39], and CellDetect), it is shown to be more efficient on various types of data made publicly available (see Section 4) still achieving similarly accurate results. [PERSON] et al. [32] propose the software **ANCIS** an Attentive Neural Cell Instance Segmentation method to predict each cell's bounding box and its segmentation mask simultaneously. The method builds on a joint network that combines the single shot multi-box detector (SSD) one-stage object detector [40] and U-net [37] for cell segmentation. Attention mechanisms are adopted in detection and segmentation modules to focus the model on useful features while suppressing irrelevant information. The software, tested on DIC images of neural cells, is publicly available (see Section 3). In [24], [PERSON] and [PERSON] use a watershed [41] marker-based approach with two convolutional neural networks (CNN) of hour-glass architecture shape to segment clustered cells in images consisting of five datasets, three of which originate from the Cell Tracking Challenge [22] (DIC-C2 DH-HeLa, Fluo-N2 DH-SIM+, and PhC-C2 DL-PSC). They used normalization by histogram equalization and median scaling as a pre-processing step. Afterwards, they augment the data by randomized rigid geometric transformations and scaling. Then, [PERSON] et al. use one CNN to predict cell marker pixels and the other CNN for image foreground predictions. They utilize these outputs to compute the marker function to obtain the segmentation seeds and the segmentation function to define cell regions that are further used in the marker-controlled watershed segmentation. [PERSON] et al. [42] present a method for segmenting touching cells in BF images from the Cell Tracking Challenge [22] by using a novel representation of cell borders, inspired by distance maps. The proposed method uses an adapted U-Net with two decoder paths, one for prediction of cell distance and another for prediction of neighbor distance. These distances are then used for the watershed-based post-processing to obtain segmentations. Results are evaluated using the SEG, DET, and CSB metrics (see Section 5) and show accurate performances, with average SEG and DET scores of 0.726 and 0.975, respectively. [PERSON] et al. [43] propose a weakly supervised cell instance segmentation method that recognizes each cell region by only using weak labels, i.e., point-level (cell centroid positions) rather than pixel-level annotations, as training data. This approach strongly reduces the annotation cost compared with the standard annotation method required for supervised segmentation. They train a cell detection CNN (U-net) and then use it to estimate rough cell positions. The rough cell shapes are extracted from the detection network by backpropagating the activation from output to input, obtaining a relevance map that shows how each pixel in the input image is relevant to the output. The final cell shapes are estimated by graph-cut [44] using the estimated relevance map as a seed. Results on different datasets show that the method works well with different types of microscopy and different contrasts. [PERSON] and [PERSON] introduce **Cellpose**[45] a software library for the instance segmentation of cell images. It implements a CNN using the U-net architecture style. Cellpose provides the probability of a pixel being inside a cell and the flows of pixels in \(xy\) coordinates towards the cell center. The flows are then used to construct the cell ROIs. Several results confirm its reliability on a wide range of label-free images without model retraining or parameter adjustment.