Linux

The macro will segment nuclei and separate clustered nuclei in a 3D image using a 2D Gaussian blur, followed by Thresholding, 2D hole filling and a 2D watershed. As a result an index-mask image is written for each input image.

U-Net segmentation as presented in Reference Publication. The model predicts three classes: background, edge and foreground. The model was trained with Kaggle Data Science Bowl (DSB) 2018 training set.

Nuclei Segmentation using Deep Learning for individual cell analysis (DeepCell).

This plugin computes for each image element (pixel/voxel) the eigenvalues of the Hessian, which can be used for example to discriminate locally between plate-like, line-like, and blob-like image structures

3D spot detection using the Determinant of Hessian (DoH) and the detection of 3D minima.