A collection is a software that encapsulate a set of bioimage components and/or workflows.

Mask-RCNN

Description

This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.

sumproduct

Description

An implementation of Belief Propagation for factor graphs, also known as the sum-product algorithm

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pydensecrf

Description

This is a (Cython-based) Python wrapper for Philipp Krähenbühl's Fully-Connected CRFs (version 2).

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PyTorch

Description

PyTorch is an open-source machine learning library for Python, based on Torch, used for applications such as natural language processing.

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pyBPDL

Description

The Binary Pattern Dictionary Learning (BPDL) package is suitable for image analysis on a set/sequence of images to determine an atlas of a compact region. In particular, the application can be maping gene activation accross many samples, brain activations in a time domain, etc.

Atlas

BIRL: Benchmark on Image Registration methods with Landmark validation

Description

The project introduces a cross-platform framework for comparison of image registration methods with landmark validation (registration precision is measured by user landmarks). The project contains a set of sample images with related landmark annotations and experimental evaluation of state-of-the-art image registration methods.

Some key features of the framework:

  • automatic execution of image registration of a sequence of image pairs
  • integrated evaluation of registration performances using Target Registration Error (TRE)
  • integrated visualization of performed registration
  • running several image registration experiment in parallel
  • resuming unfinished sequence of registration benchmark
  • handling around dataset and creating own experiments
  • rerun evaluation and visualisation for finished experiments
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pyImSegm

Description

Collection of several basic standard image segmentation methods focusing on medical imaging. In particular, the key block/applications are (un)supervised image segmentation using superpixels, object centre detection and region growing with a shape prior. Besides the open-source code, there is also a few sample images.

 

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Maximum (3D) VIB Protocol

Description

This component convolves the image with maximum filter. Each voxel is set to the maximum value of its neighborhood. The neighborhood is defined by a kernel, which has a diameter of 3 voxels.

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ImageJ Built-in Macro Functions

Description

A set of ImageJ Built-in Macro Functions used to perform operations on the ImageJ platform.

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Thresholder (ImageJ)

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