DeepCell

Description

 

DeepCell is neural network library for single cell analysis, written in Python and built using TensorFlow and Keras.

DeepCell aids in biological analysis by automatically segmenting and classifying cells in optical microscopy images. This framework consumes raw images and provides uniquely annotated files as an output.

The jupyter session in the read docs are broken, but the one from the GitHub are functional (see usage example )

deepcell

PYME

Description

The PYthon Microscopy Environment is an open-source package providing image acquisition and data analysis functionality for a number of microscopy applications, but with a particular emphasis on single molecule localisation microscopy (PALM/STORM/PAINT etc ...). The package is multi platform, running on Windows, Linux, and OSX.

It comes with 3 main modules:

  • PYMEAcquire - Instrument control and simulation
  • dh5view - Image Data Analysis and Viewing
  • VisGUI - Visualising Localization Data Sets

CellProfiler Analyst CPA

Description

CellProfiler Analyst (CPA) allows interactive exploration and analysis of data, particularly from high-throughput, image-based experiments. Included is a supervised machine learning system which can be trained to recognize complicated and subtle phenotypes, for automatic scoring of millions of cells. CPA provides tools for exploring and analyzing multidimensional data, particularly data from high-throughput, image-based experiments analyzed by its companion image analysis software, CellProfiler.

CPA

Snakemake

Description

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. 

YeastSpotter

Description

Code to segment yeast cells using a pre-trained mask-rcnn model. We've tested this with yeast cells imaged in fluorescent images and brightfield images, and gotten good results with both modalities. This code implements an user-friendly script that hides all of the messy implementation details and parameters. Simply put all of your images to be segmented into the same directory, and then plug and go.

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MSRC Registration Toolbox

Description

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:

  • Advanced Registration and Analysis of MALDI Imaging Mass Spectrometry Measurements through Autofluorescence Microscopy10.1021/acs.analchem.8b02884

  • Next Generation Histology-directed Imaging Mass Spectrometry Driven by Autofluorescence Microscopy10.1021/acs.analchem.8b02885

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Nuclei Segmentation (ilastik)

Description

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

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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.

Nuclei Segmentation (Mask-RCNN)

Description

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.

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Landmark detection DMBL model prediction

Description

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

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