Cellpose allows to segment cells of different types from different imaging modalities. The deep-learning network is pre-trained with images of 70000 cells. Cellpose uses a vector flow representation of cells. The input is  an image of the cells and optionally an image of stained nuclei.

MRI added a batch processing button, which allows to run the batch segmentation of cells from the graphical user interface instead of using the python from the command-line.

See also:

• Cellpose (Presentation for the MRI image analysis group,  05.02.2020)
• Cellpose reloaded (Presentation for the MRI image analysis group, 04.03.2021)

Noise2Void is a deep-learning method that can be used to denoise microscopy images. No specific training datasets are required, only your noisy images. One noisy image is sufficient to train a network. Use dl4mic to train and apply the Noise2Void.

Automatic detection and segmentation of cells and nuclei using star-convex polygons.

Use dl4mic to train and apply StarDist.

UNet is a deep-learning method for pixel classification. It can be trained to segment different kinds of objects in images. The first half of the U-Net architecture is a downsampling convolutional neural network which acts as a feature extractor from input images. The other half upsamples these results and restores an image by combining results from downsampling with the upsampled images. Use dl4mic to train and apply the UNet.