A workflow is a set of components assembled in some specific order to

  1. Measure and estimate some numerical parameters of the biological system or
  2. Visualization

for addressing a biological question. Workflows can be a combination of components from the same or different software packages using several scripts and manual steps.

CAREless

Description

Deep learning based image restoration methods have recently been made available to restore images from under-exposed imaging conditions, increase spatio-temporal resolution (CARE) or self-supervised image denoising (Noise2Void). These powerful methods outperform conventional state-of-the-art methods and leverage down-stream analyses significantly such as segmentation and quantification.

To bring these new tools to a broader platform in the image analysis community, we developed a simple Jupyter based graphical user interface for CARE and Noise2Void, which lowers the burden for non-programmers and biologists to access these powerful methods in their daily routine.  CARE-less supports temporal, multi-channel image and volumetric data and many file formats by using the bioformats library. The user is guided through the different computation steps via inline documentation. For standard use cases, the graphical user interface exposes the most relevant parameters such as patch size and number of training iterations, while expert users still have access to advanced parameters such as U-net depth and kernel sizes. In addition, CARE-less provides visual outputs for training convergence and restoration quality. Any project settings can be stored and reused from command line for processing on compute clusters. The generated output files preserve important meta-data such as pixel sizes, axial spacing and time intervals.

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Neuron Tracing Vaa3D (BJUT FM Spanning Tree)

Description

Vaa3d BJUT Fast Marching Spanning Tree algorithm dockerised workflow for BIAFLOWS

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Filament Tracing LocThresh (ImageJ)

Description

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,

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Filament Tracing Tubeness (ImageJ)

Description

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.

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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 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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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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Object Tracking (Octave)

Description

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

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