## 1 Introduction  
Advances in microscopy imaging technologies have enabled the visualization of dynamic live-cell processes using time-lapse microscopy methodologies [1; 2; 3]. Therefore, collections of microscopy images have become a primary source of data to unravel the complex mechanisms and functions of living cells [4]. The vast quantity and complexity of the data generated by modern visualization techniques preclude visual or manual analysis, therefore requiring computational methods to infer biological knowledge from large datasets [5; 6]. In particular, the objects of interest in cellular images are characterized by high variations of morphology and intensity from image to image [6], which make features such as cell boundaries and intracellular features difficult to accurately identify. For this reason, cell segmentation analysis has gained increasing attention over the last decade [7]. The most commonly used free and open-source software tools for microscopy applications in the laboratory are Imagej [8] or Fiji [9], and CellProfiler [10]. Although these tools offer customization capabilities, they do not provide suitable functionalities for fast and efficient high-throughput cell image analysis on large-scale datasets. In addition, CellProfiler Analyst [11] allows the user to explore and visualize image-based data and to classify complex biological phenotypes with classic supervised Machine Learning (e.g., Random Forests, Support Vector Machines). Taken together, these tools provide accurate performance for image quantification on high-quality annotated datasets but generally lack capabilities to work in the laboratory practice, because training and model setup phases are required; in addition, the user is often forced to transfer data from one tool to another for achieving the desired analysis outcome. Therefore, this hinders these tools to match the time constraints imposed by time-lapse microscopy studies. Various mathematical morphology methodologies have been extensively used in cell imaging to tackle the problem of cell segmentation [7]. [PERSON] et al. [12] presented a region-based segmentation approach in which both foreground and background seeds are used as starting points for the watershed segmentation of the gradient magnitude image. Since more than one seed could be assigned to a single object, initial over-segmentation might occur. In [13], the authors developed an automated approach based on the Voronoi tessellation [14] built from the centers of mass of the cell nuclei, to estimate morphological features of epithelial cells. Similarly to the approach presented in this paper, the authors leveraged the watershed algorithm to correctly segment cell nuclei. It is worth noting that we do not exploit Voronoi tessellation since our objective disregards the morphological properties of adjacent cells in a tissue. [PERSON] et al. [15] introduced an approach based on globally optimal models for cell nuclei segmentation, which exploits both shape and intensity information of fluorescence microscopy images. This globally optimal model-based approach relies on convex level set energies and parameterized elliptical shape priors. Differently, no detection with ellipse fitting was exploited in [16], as this shape prior is not always suitable for representing the shape of the cell nuclei because of the highly variable appearance. In particular, a two-stage method combining the split-and-merge and watershed algorithms was proposed. In the first splitting stage, the method identifies the clusters by using inherent characteristics of the cell (such as size and convexity) and separates them by watershed. The second merging stage aims at detecting the over-segmented regions according to the area and eccentricity of the segmented regions. These sub-divisions are eliminated by morphological closing. Machine Learning techniques have been applied to bioimage analysis [17; 18].