Contents

On the home page the links in the Category panel (Training materials/Dataset/Software) all points to the advanced search page. 

I dont think this is expected, the panel below works fine though.

Video and detailed protocol for setting up a multi-view, multi-channel,  time-lapse light-sheet imaging experiment using zebrafish embryos. As well as the necessary image processing for registration and fusion / deconvolution of the generated image data. 

A command line tool that allows to quantitatively compare two volumes of binary segmentations. Implements 22 different metrics for comparing segmentations such as Dice Coefficient, Hausdorff Distance and average Distance. 

A Python based workflow management software that allows to create workflows that seamlessly scale from a single workstation to a high performance computing cluster or cloud environments. 

Automated workflow for performing multiview reconstruction of large multiview, multichannel, multiillumination time-lapse SPIM data on a high performance computing (HPC) cluster or on a single workstation. 

This python toolbox performs registration between 2-D microscopy images from the same tissue section or serial sections in several ways to achieve imaging mass spectrometry (IMS) experimental goals.

This code supports the following works and enables others to perform the workflows outlined in the following works, please cite them if you use this toolbox:

NEUBIAS-WG5 workflow for nuclei segmentation using ilastik v1.3.2 and Python post-processing.

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.

NEUBIAS-WG5 workflow for nuclei segmentation using Mask-RCNN. The workflow uses Matterport Mask-RCNN. Keras implementation. The model was trained with Kaggle 2018 Data Science Bowl images.

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

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

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

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

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

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.