## 1 Introduction  
_S. cerevisiae_, hereafter referred to as yeast, is a eukaryotic model organism used to study synthetic gene network development and analysis, as well as other biological processes. Bright-field and fluorescence microscopy are common approaches to investigate yeast behavior. The quantitative analysis of time-lapse fluorescence microscopy images is a powerful tool for large-scale and single-cell analysis of dynamic and noisy cellular processes, such as gene expression [1; 2; 3]. In an experimental design wherein yeast cells are expressing a fluorescent marker of interest, quantitative analysis involves the quantification of fluorescence intensity of pixels corresponding to cell regions in a microscopy image of those cells. Therefore, quantitative analysis requires accurate identification of cell regions, known as segmentation, as well as tracking of these cell regions between images, in the case of time-lapse microscopy. In recent years, automated light microscopes have been paired with commercial or lab-constructed microfluidics devices to conduct time-lapse analysis of cell cultures in long-term perfusion conditions. Microfluidics-enabled time-lapse fluorescence microscopy allows the study of dynamic cellular processes in a single-cell manner. Automated image capturing of multiple fields of view at high imaging frequencies increases the number of tracked cells and enhances the resolution of the data. However, it also greatly increases the number of images that need to be analyzed from hundreds to tens of thousands. Thus,automated solutions for the problem of cell segmentation are necessary to avoid very time-consuming and error prone manual segmentation. One way to address the segmentation problem has been the use of fluorescent markers expressed in the cytosol to label cells in fluorescence images. For example, cells with constitutively expressed green fluorescent protein (GFP) are easily separable from a background when imaged with a green light filter. Cell segmentation in fluorescence images is easier than that in corresponding bright-field images; however, the number of different fluorescent proteins (FPs) that can be used in an experiment is limited [4]. Due to overlapping excitation and emission spectra of many FPs, as well as the effects of Forster resonance energy transfer (FRET), using multiple different FPs requires careful selection. Moreover, additional fluorescent imaging increases the risk of phototoxicity and photobleaching. Although use of fluorescent markers can be beneficial in some cases, cell segmentation from bright-field images is required in most cases. Therefore, the ideal algorithm for cell segmentation should work automatically from a bright-field image input. Several features of yeast cells in bright-field images have been used for segmentation by non-trainable (manually parameterized) algorithms or manual analysis. A cell's outline is generally its most distinguishable 2D structure in a bright-field image due to the distinct brightness of the outline relative to the cell interior and background. In slightly out-of-focus bright-field images, this outline is thicker and easier to identify using thresholding techniques [5]. However, several characteristics of yeast cells prevent accurate segmentation with these traditional algorithms, particularly in the case of time-lapse microscopy wherein distinguishable features of cell outlines usually change over time. Yeast cells divide quickly, forming colonies of tightly packed cells within hours. In such dense colonies neighboring cells converge or overlap, making detection of individual outlines very difficult. Additionally, cell outlines may change in brightness and thickness due to focus drift of the microscope over time. The history of image segmentation algorithms is long and varied. The simplest method of automated image segmentation is thresholding, in which a brightness threshold is manually selected and every pixel in an image is classified as foreground if it is higher or background if it is lower.