TABLE 1:

Overview of deep-learning software for bioimage segmentation.

Tool	Website	Available tasks	Bioimage types	For testing or training?	Software format	Usage instructions	Citation
CDeep3M2 Cloud-based image segmentation tasks with pretrained models available for electron micrographs	https://cdeep3m.crbs.ucsd.edu/cdeep3m	Semantic segmentation	Light x-ray Electron microscopy	Web app: testing for electron micrograph segmentation Other options: both	Web app Google Colab Docker container app Singularity container app AWS cloud	FAQ GitHub README and Wiki	Haberl et al., 2018 Haberl et al., 2020
CellPose Nuclear and cytoplasmic segmentation in a web app or local installation with integrated annotation tools for training	http://www.cellpose.org/	Instance segmentation of nuclei and/or cytoplasm	Fluorescence Brightfield	Web app: testing Local installation: testing and training Annotation integrated into GUI	Web app Local installation with GUI or command line interface Jupyter Notebook Google Colab	Documentation website GitHub README	Stringer et al., 2020
CSBDeep Toolbox of multiple models in ImageJ plug-ins and Jupyter notebooks	https://csbdeep.bioimagecomputing.com/	Instance segmentation Restoration Combined denoising and instance segmentation	Fluorescence Brightfield (H&E) Electron micrographs	Testing and training	ImageJ plugins Jupyter Notebook	Overview website Jupyter Notebook text cells Exercise sheets Video tutorials	Broaddus et al., 2020 Buchholz et al., 2020 Krull et al., 2019 Schmidt et al., 2018 Weigert et al., 2018
DeepCell Web application for applying already-trained segmentation and tracking models on new images	https://deepcell.org/	Semantic segmentation Tracking	Fluorescence Phase	Testing	Web app	FAQ	Bannon et al., 2018
DeepImageJ Collection of models within one ImageJ plug-in interface	https://deepimagej.github.io/deepimagej/	Semantic and instance segmentation Denoising Reconstruction Superresolution Virtual labeling Single molecule localization	Fluorescence DIC Phase contrast Light transmission microscopy	Testing	ImageJ plug-in	User guide Video tutorials	Gómez-de-Mariscal et al., 2019
DeepMIB MATLAB-based tool for segmentation in two or three dimensions	http://mib.helsinki.fi/tutorials_segmentation.html	Semantic segmentation	Electron microscopy Brightfield Fluorescence	Testing and training	Matlab app Standalone version (no Matlab needed)	User guide Video tutorials	Belevich and Jokitalo, 2021
HistomicsML2 Interactive segmentation for WSIs with integrated active learning	https://github.com/CancerDataScience/HistomicsML2	Semantic segmentation	WSIs Histological images Brightfield	Testing and training	Docker container app	Documentation website Video tutorial	Lee et al., 2020
InstantDL Python-based pipeline for classification and segmentation	https://github.com/marrlab/InstantDL	Semantic segmentation Instance segmentation Pixel-wise regression Two-dimensional classification	Fluorescence Brightfield Medical imaging (CT scan)	Testing and training	Local installation with command line and config file interface Docker container app	GitHub README	Waibel et al., 2020
NucleAlzer Nuclear segmentation in a wide variety of image types in a web app or local app containing command line scripts	https://www.nucleaizer.org/	Instance segmentation of nuclei	Fluorescence Immunohistochemistry Hematoxylin & eosin	Web app: testing Local installation: testing and training	Web app Local installation with command line interface	Explanatory text on web app	Hollandi et al., 2020
ZeroCostDL4Mic Collection of ready-to-use Google Colab notebooks for various image analysis tasks	https://github.com/HenriquesLab/ZeroCostDL4Mic	Semantic segmentation Instance segmentation Denoising Image-to-image translation Object detection	Fluorescence Electron microscopy	Testing and training	Google Colab GPU available through Google	GitHub Wiki Colab notebook text cells Video tutorials	Von Chamier et al., 2020

The described tools are designed for scientists without formal computational training. Definitions: DIC, differential interference contrast; GUI, graphical user interface, a visual method for interacting with software; GPU, graphics processing unit, a specialized type of processor that improves performance, especially for three-dimensional data sets and training models; Jupyter Notebook, interactive notebook to run code interspersed with explanatory text; Google Colab, interactive notebooks hosted in the cloud by Google; WSI, whole slide image.