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VAST (Volume Annotation and Segmentation Tool) is a utility application for manual annotation of large EM stacks.

General labeling tool, used for a large variety of 3D data sets; electron-microscopic, multi-channel light-microscopic, and Micro-CT data sets as well as videos, and annotating arbitrary structures, regions and locations, depending on the user’s needs.

Vaa3d BJUT Fast Marching Spanning Tree algorithm dockerised workflow for BIAFLOWS

Blood vessels tracing in 3D image from 3D Gaussian blurring (user defined radius), local thresholding (user defined radius and offset) and 3D skeletonization. Dockerized version for BIAFLOWS,

Blood vessels tracing in 3D image from Tubeness filtering (user defined scale), 3D opening (radius set to 2), thresholding (user defined level) and 3D skeletonization.

3D Neuron Tracing with a Dockerized version of Vaa3D MOST Raytracer.

3D Neuron Tracing using Dockerized version of Vaa3D Minimum Spanning Tree (MST).

Rivuletpy dockerised workflow for BIAFLOWS.

Vaa3d All-Path-Pruning 2.0 (APP2) dockerised workflow for BIAFLOWS.

Cell tracking using MU-Lux-CZ algorithm. Dockerized Workflow for BIAFLOWS implemented by Martin Maska (Masaryk University).

Nuclei tracking in 2D time-lapse with Octave tracker (adapted from Matlab LOBSTER version).

Object tracking. For each time-frame, an image mask is obtained from median filtering (user defined radius), thresholding (user defined level) and hole filling. Convex objects are split apart by distance map watershed from regional intensity maxima (user defined noise tolerance), eroded (user defined radius) and analyzed as 3D particles (assuming some overlap between objects from a frame to the next frame). Finally, division events are analyzed and accounted for to relabel objects.

pyimagej provides a set of wrapper functions for integration between ImageJ and Python.

It also provides a high-level entry point for invoking ImageJ server APIs.

Track non-dividing particles in 2D time-lapse image.

Particle tracking in 2D time-lapse based on linking closest regional intensity minima (user defined noise tolerance) detected from Laplacian of Gaussian filtered images (user defined radius). A maximum linking distance is set (user defined).

Execute Nuclei Segmentation in 3D images using pixel classification with ilastik.

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.