Contents

This workflow predict landmark positions on images by using MSET landmark detection models.

This workflow trains MSET landmark detection models from a dataset of annotated images.

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

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

This workflow segments glands from H&E stained histopathological images
from the Gland Segmentation Challenge (GlaS2015) using deep learning (UNet).
UNet implementation largely inspired from PyTorch-UNet by Milesial. 

ImageJ/FIJI plugin generating contour lines with equal spacing on top of an image (using overlay).

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.

BIRL stands for "Benchmark on Image Registration methods with Landmark validation". BIRL is 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:

This dataset aims at the comparison of the automatic nonlinear image registration of 2D microscopy images of histopathology tissue samples stained with different dyes. 

jSLIC superpixels - is a segmentation method for clustering similar regions - superpixels - in the given image which are usually used for other segmentation techniques. The only two parameters are average (initial) size of each superpixel and rigidity parameter in range (0,1)

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.

VascuSynth is an ITK-based synthetic image generator. It synthesizes volumetric images of vascular trees and generates a .gxl file of the ground-truth tree structure. VascuSynth receives a number of .txt configuration files and is capable of generating both ground truth ('ideal') images and images with added noise. The user is capable of choosing from a set simple noise additions and artefacts.

This workflow processes a group of images containing cells with discernible nuclei and segments the nuclei and outputs a binary mask that show where nuclei were detected. It was developed as a test workflow for Neubias BIAFLOWS Benchmarking tool.

Runs fill holes operation on 3D images.

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.

This workflow processes images of cells with discernible nuclei and outputs a binary mask containing where nuclei are detected.

ROI measurement plug-in for Icy.