plugin

Description

BaSiC is a software tool for Background and Shading correction of Optical Microscopy Images. It implements an image correction method based on low-rank and sparse decomposition to solve both shading in space and background variation in time. It can correct temporal drift in time-lapse microscopy data and thus improve continuous single-cell quantification. BaSiC is available as a Fiji/ImageJ plugin.

 

A BaSiC Tool for Background and Shading Correction of Optical Microscopy Images
Description

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

Quote:

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.

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Description

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.

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annotatorJ
Description

The tool exports rectangular regions, defined with the NDP.view 2 software (hammatsu) from the highest resolution version of the ndpi-images and saves them as tif-files.

Click the button and select the input folder. The input folder must contain pairs of ndpi and ndpa files. The regions will be exported to a subfolder of the input folder names zones.

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imagej toolset to export regions from ndpi and ndpa-files
Description

This Fiji plugin is a python script for CLEM registration using deep learning, but it could be applied in principle to other modalities. The pretrained model was learned on chromatin SEM images and fluorescent staining, but a script is also provided to train an new model, based on CSBDeep. The registration is then performed as a feature based registration, using register virtual stack plugin (which extract features and then perform RANSAc. Editing the script in python gives access to more option (such as the transformation model to be used, similarity by default. Images need to be prepared such that they contain only one channel, but channel of ineterst (to be transformed with the same transformation) can be given as input, and Transform Virtual Stack plugin can be used as well.

F1000R Figure 1 DeepCLEM