Focused ion beam scanning electron microscopy

Synonyms
FIB-SEM
Description

 

Relate is a correlative software package optimised to work with EM, EDS, EBSD, & AFM data and images.  It provides the tools you need to correlate data from different microscopes, visualise multi-layered data in 2D and 3D, and conduct correlative analyses.

  • Combining data from different imaging modalities (e.g. AFM, EDS & EBSD)

  • Interactive display of multi-layer correlated data

  • Analytical tools for metadata interrogation

  • Documented workflows and processes

Correlate

  • Import data from AZtec using the H5oina file format
  • Import AFM data
  • Correlate both sets of data using intuitive image overlays and image matching tools
  • Produce combined multimodal datasets

Visualise

  • 2D display of multi-layered data
  • 3D visualisation of topography combined with AFM material properties, EM images, and EDS & EBSD map overlays
  • Customisation of colour palettes, data overlays, image rendering options, and document display
  • Export images and animations

Analyse

  • Generate profile (cross section) views of multimodal data
  • Measure and quantify data across multiple layers
  • Analyse areas via data thresholding using amount of x-ray counts, phase maps, height, or other material properties.
  • Select an extensive range of measurement parameters
  • Export analytical data to text or CSV files
Relate analysis workflow example
Description

Hot-Knife is a library specifically designed for FIB-SEM data and thick sections, which includes code for flattening, deformable alignment with features and a more robust kind of block-matching, and some visualization, manual correction, and import and export tools (n5).

Description

VAST (Volume Annotation and Segmentation Tool) is a utility application for manual annotation of large EM stacks.

General labeling tool, used for a large variety of 3D data sets; electron-microscopic, multi-channel light-microscopic, and Micro-CT data sets as well as videos, and annotating arbitrary structures, regions and locations, depending on the user’s needs.

Description

Super-resolve anisotropic EM data along low-res axis with deep learning.

 

has function
Description

SuRVoS: Super-Region Volume Segmentation workbench

A volume is first partitioned into Super-Regions (superpixels or supervoxels) and then interactively segmented by the user providing training annotations. SuRVoS can then learn from and extend the annotations to the whole volume.

User interface of SuRVoS showing example annotation on soft x-ray tomography data