e-learning
Segmentation of Anatomical Structures in Medical 3-D Images
Abstract
Image data in medical imaging is often stored and exchanged in the DICOM file format. A DICOM dataset is a file that contains rich metadata (patient, study info) and the actual medical image data stored as pixels (for 2-D images) or voxel (for 3-D images). The image data can be single-channel or multi-channel, and it can also be organized in multiple frames (e.g., spatial tiles of a mosaic, z-slices of a 3-D image, or time steps).
About This Material
This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.
Questions this will address
- How do I use Galaxy with DICOM data?
- How do I inspect 3-D image data in Galaxy?
- How do I perform image analysis with 3-D image data in Galaxy?
- How do I visualize 3-D segmentation results in Galaxy?
Learning Objectives
- Learn handling and navigating 3-D image data in Galaxy.
- Learn to use generic image analysis tools for analysis of medical images.
Licence: Creative Commons Attribution 4.0 International
Keywords: 3D image, Computed tomography, Conversion, Image segmentation, Imaging, Medical imaging, Object feature extraction, Overlay, Volume rendering
Target audience: Students
Resource type: e-learning
Version: 1
Status: Active
Prerequisites:
- FAIR Bioimage Metadata
- Introduction to Galaxy Analyses
- REMBI - Recommended Metadata for Biological Images – metadata guidelines for bioimaging data
Learning objectives:
- Learn handling and navigating 3-D image data in Galaxy.
- Learn to use generic image analysis tools for analysis of medical images.
Date modified: 2026-02-26
Date published: 2026-02-26
Contributors: Beatriz Serrano-Solano, Diana Chiang Jurado
Scientific topics: Imaging
Activity log
