Neuron Tracing Vaa3D (BJUT FM Spanning Tree)

Description

Vaa3d BJUT Fast Marching Spanning Tree algorithm dockerised workflow for BIAFLOWS

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Neuron Tracing Vaa3D (MOST)

Description

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

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Neuron Tracing 3D (Rivuletpy)

Description

Rivuletpy dockerised workflow for BIAFLOWS.

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Neuron Tracing Vaa3D (App2)

Description

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

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Paintera

Description

Paintera is a general visualization tool for 3D volumetric data and proof-reading in segmentation/reconstruction with a primary focus on neuron reconstruction from electron micrographs in connectomics. It features/supports:

  •  Views of orthogonal 2D cross-sections of the data at arbitrary angles and zoom levels
  •  Mipmaps for efficient display of arbitrarily large data at arbitrary scale levels
  •  Label data
    •  Painting
    •  Manual agglomeration
    •  3D visualization as polygon meshes
      •  Meshes for each mipmap level
      •  Mesh generation on-the-fly via marching cubes to incorporate painted labels and agglomerations in 3D visualization. Marching Cubes is parallelized over small blocks. Only relevant blocks are considered (huge speed-up for sparse label data).

Paintera is implemented in Java and makes extensive use of the UI framework JavaFX

Paintera screenshot

CEM

Description

Computer-assisted Evaluation of Myelin formation (CEM) is a collection designed to automate myelin quantification. It requires use input to choose the best threshold values. The myelin is calculated as an overlap between neuronal signal and oligodendrocyte signal. Results are given as pixel counts and percents.

CEM runs as an imageJ plugin with an optional Matlab extension to remove cell bodies. More details are published at Kerman et al. 2015 Development. Supplemental Material includes a detailed user manual and the download link.

Myelin

VMTK: Vascular Modeling Toolkit

Description

vmtk is a collection of libraries and tools for 3D reconstruction, geometric analysis, mesh generation and surface data analysis for image-based modeling of blood vessels.

vmtk is composed of

  • C++ classes (VTK and ITK -based algorithms)
  • Python classes (high-level functionality - each class is a script)
  • PypeS - Python pipeable scripts, a framework which enables vmtk scripts to interact with each other