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The empanada-napari plugin is built to democratize deep learning image segmentation for researchers in electron microscopy (EM). It ships with MitoNet, a generalist model for the instance segmentation of mitochondria. There are also tools to quickly build and annotate training datasets, train generic panoptic segmentation models, finetune existing models, and scalably run inference on 2D or 3D data. To make segmentation model training faster and more robust, CEM pre-trained weights are used by default. These weights were trained using an unsupervised learning algorithm on over 1.5 million EM images from hundreds of unique EM datasets making them remarkably general.

Removal of heterogeneous background from image data of single-molecule localization microscopy, using extreme value-based emitter recovery (EVER).

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EVER requires no manual adjustment of parameters and has been implemented as an easy-to-use ImageJ plugin that can immediately enhance the quality of reconstructed super-resolution images. This method is validated as an efficient way for robust nanoscale imaging of samples with heterogeneous background fluorescence, such as thicker tissue and cells.

The napari-pyclesperanto-assistant is a yet experimental napari plugin for building GPU-accelerated image processing workflows targeting life-sciences and bio-image analysis. It is part of the clEsperanto project. It uses pyclesperanto and pyopencl as backend for processing images.

AnnotatorJ is a Fiji Plugin to ease annotation of images, particulrly useful for Deep Learning or to validate an alogorithm. Interestingly, it allows annotation for instance segmentation, semantic segmentation, or bounding box annotations. It includes toolssuch as active contours to ease these annotations.

Machine Learning made easy

APEER ML provides an easy way to train your own machine learning
models and segment your microscopy images. No expertise or coding required.