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 Vaa3D (MST)

Description

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

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Neuron Tracing Vaa3D (App2)

Description

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

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Object Tracking (MU-Lux-CZ)

Description

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

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Nuclei Tracking (TrackMate)

Description

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

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Nuclei Segmentation 3D (Ilastik)

Description

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

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Nuclei Segmentation (U-Net)

Description

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.

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Nuclei Segmentation (DeepCell)

Description

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

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Napari image viewer

Description

napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing large multi-dimensional images. It’s built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (e.g. numpyscipy). It includes critical viewer features out-of-the-box, such as support for large multi-dimensional data, and layering and annotation. By integrating closely with the Python ecosystem, napari can be easily coupled to leading machine learning and image analysis tools (e.g. scikit-imagescikit-learnTensorFlowPyTorch), enabling more user-friendly automated analysis.

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