Free but not open source

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. 

Holovibes is a free software dedicated to the calculation of holograms in real-time. Input interferogram data can be grabbed from a digital camera or loaded from files recorded beforehand. Massive amounts of data can be handled robustly at high throughput, saved to disk, and visualized in real-time without any risk of frame dropping thanks to the use of several configurable input and output memory buffers.

Main features

Image acquisition from several digital cameras or from data files
Choice of hologram rendering method
Blazing-fast hologram rendering
Real-time computation of spectrograms
Hologram autofocus
Image and video post-processing
High throughput saving to disc of massive datasets
Batch recording and communication with remote instruments via GPIB

Requirements

A PC with at least 8 GB of RAM
Microsoft Windows 7/10 64-bit operating system
A NVidia graphics card (GeForce GTX 700+ series)
NVidia CUDA 9
A supported digital camera, or raw interferogram files

Use case examples

Holographic microscopy
Holographic OCT
Holographic vibrometry
Holographic angiography
Holographic plethysmography

This workflow describes a semi-automatic image segmentation procedure for 3D reconstructions of the coronary arterial tree, after which how different morphometric features are automatically extracted, including vessel lumen diameter of the three main coronaries.

Image correction software for chromatic shifts in fluorescence microscopy