## 3 Software  
As also already discovered in Section 2, it is every day more common for newly proposed methods to make their implementations publicly available, in the light of the recent trend toward open science. In Table 1, we provide links to existing publicly available software, subdivided by task. Moreover, we provide links to software platforms, providing more diverse functionalities for analyzing microscopy images and videos. Besides the software already described in Section 2, here we briefly summarize the remaining ones. **TWS** (Trainable Weka Segmentation) is a Fiji plugin that combines ML algorithms with a set of selected image features to produce pixel-based segmentations. Weka (Waikato Environment for Knowledge Analysis) [66] can itself be called from the plugin. **Baxter Algorithms** is a software package for tracking and analyzing cells in microscope images, providing an implementation of the global track-linking algorithm in [60]. The software can handle images produced using both 2D transmission microscopy and 2D or 3D fluorescence microscopy. **CellProfiler** is a commonly used program designed for biologists with minimal programming knowledge to measure biological phenotypes quantitatively [39]. Algorithms for image analysis are available as individual modules that can be placed in sequential order to create a pipeline. Several commonly used pipelines are available for download and can be used to detect and measure various properties of biological objects. **liastik**[38] is an interactive machine learning tool based on a random forest classifier [67] for image analysis and is widely used by biologists since it no require specific ML knowledge. It provides pipelines for segmentation, classification, tracking, and lineage, performing on multidimensional data (including 3D space, time, and channels). A friendly user interface enables users to interactively implement their image analysis through a supervised machine learning workflow. liastik classifies pixels and objects by learning from annotations to predict the class of each unannotated pixel and object. It provides an automatic selection of image features based on a first optimization step. Users can introduce sparse annotations or use labeled data or even provide training examples, then correct the classifier precisely at the position where it is wrong. Once a classifier has been trained, new data can be processed in batch mode. **ZeroCostDL4 Mic** is a cloud-based platform proposed by [PERSON] et al. [69] aiming to simplify the use of DL architectures for various microscopy tasks. It is a collection of Jupyter Notebooks that can efficiently and interactively run Python code, leveraging the free, cloud-based computational resources of Google Colab. Concerning our focus, the tasks covered by ZeroCostDL4 Mic include object detection, for which it implements YOLOv2, and cell segmentation, where it implements both the U-net and StarDist [74; 75] networks. The outputs generated by StarDist are directly compatible with the TrackMate tracking software, enabling also automated cell tracking. **DeepImageJ**[70] is a plugin for Imagej and Fiji to facilitate the usage of DL models. It aims to offer user-friendly access to pre-trained models designed for various image modalities, including PhC and DIC. Currently, for the two previously mentioned modalities, the DL models are designed for segmentation. **BioImage Model Zoo**[71] is an online repository for AI models to facilitate the usage of these pre-trained models by the bioimaging community. They provide a standard and tutorials to upload new models. The users can either download the projects in community partners' format or in user-friendly Python notebooks that can be used by anyone with the user's own dataset to perform bioimage analysis tasks. The current community partners are ilastik, ImJoy [76], Fiji [77], deepImagej, ZeroCostDL4 Mic, and HPA [78]. **LIM Tracker** is a Fiji plugin for cell tracking and analysis expressly aimed at advanced interactivity, usability, and versatility.