DragonFly

Description

Dragonfly is a software platform for the intuitive inspection of multi-scale multi-modality image data. Its user-friendly experience translates into powerful quantitative findings with high-impact visuals, driven by nuanced easy-to-learn controls.

For segmentation: It provides an engine fior machine Learning, Watershed and superpixel methods, support histological data .

It offers a 3D viewer, and python scripting capacities .

It is free for reserach use, but not for commercial usage.

DragonFly

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

Image Data Explorer

Description

The Image Data Explorer is a Shiny app that allows the interactive visualization of images and ROIs associated with data points shown in a scatter plot. It is useful for exploring the relationships between images/ROIs and associated data represented in tabular format.

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Automated workflow for parallel Multiview Reconstruction

Description

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. 

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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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.

Landmark detection DMBL model prediction

Description

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

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Landmark detection DMBL model training

Description

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

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Landmark detection LC models prediction

Description

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

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Landmark detection LC models training

Description

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

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