## 1 Introduction
Several studies describe the use of the BF channel in cell detection and automated image analysis of cell populations [12; 13]. With the aim of observing the dynamic behavior of living cells over time, LI microscopes are also equipped with a real-time imaging tool named _time-lapse_. Broadly speaking, time-lapse is a speed-up technique to observe events changing over time. This is usually realized by taking images at regular time intervals and merging them into a video. One of the most significant applications of time-lapse microscopy is cell population monitoring to study single-cell behavior in response to physiological or external stimuli and understand the underlying mechanisms. For example, in drug discovery and cancer research [14], time-lapse microscopy is used to look at cell response to anti-mitotic drugs in terms of cell division and cell death. To achieve this goal, quantitative information on cell behavior needs to be obtained and analyzed [15]. Cell proliferation, lineage, and fate are of primary  
Figure 1: Example of differences in appearance of fluorescence and phase contrast microscopy images (culture of human lymphocyte cells [9]): (**a**) fluorescence image of nuclear envelope; (**b**) fluorescence image of interior nuclei (DNA); (**c**) phase contrast image of whole cells. importance among various cellular events. Image analysis of these biological processes is usually performed manually with suitable protocols [16]. As manual analysis of a large volume of light microscopy images is slow, tedious, time-consuming, and subject to observer subjectivity, biological studies see an increased demand for reliable automatic imaging tools. As in many scientific disciplines, Artificial Intelligence (AI) has been changing how imaging data are processed and analyzed and how experiments are carried out. AI refers to artificial systems aiming to adapt previous knowledge to new situations and recognize meaning in data patterns. Machine Learning (ML) [17] is a subset of AI methods that extracts valuable features from large data sets to make predictions or decisions on unseen data. An ML algorithm is not designed to solve a specific problem but rather to train a computer to solve problems. The training is data-driven. Deep Learning (DL) is a set of ML algorithms using a multi-layer \"neural networks\" to progressively extract higher-level features from data (the number of layers is the depth of the model, hence the terminology \"deep learning\"; for a quick overview on DL key concepts for microscopy image data, the reader can refer to [18]). This review mainly focuses on AI methods for the most used traditional LI microscopy techniques, i.e., PhC, DIC, and BF, to investigate fundamental biological events. A comprehensive description of the state-of-the-art methods using QPI data and AI approaches, which are out of our focus, can be found in [19] and references therein. Few recent surveys are available in the literature covering methods for these types of microscopy images and videos. The study presented by [PERSON] et al. [20] performs a comprehensive comparison of cell image segmentation methods for the most common label-free microscopy techniques, including PhC, DIC, HMC, and QPI. The review covers traditional methods, providing only hints on DL-based segmentation methods. The authors identify an effective image segmentation pipeline composed of four main steps: _image reconstruction, foreground-background segmentation, seed-point extraction_, and _cell segmentation_. They discuss and assess the most effective combination of the above steps for the specific microscopy techniques based on software and tools available in the state-of-the-art literature. Furthermore, they compare the accuracy and efficiency of tools containing all the above four steps (named \"all-in-one\" tools) as well as software implementing only one of them. Software and data were made publicly available (see Sections 3 and 4). In [21], [PERSON] et al. present a review of methods and tools for cell tracking.