📋 Content
- 📋 Content
- 📄 Description
- 📣 Current announcements
- ❗ Course requirements
- 📒 Syllabus
- 📝 Theoretical basics
- ☝️ References
📄 Description
This directory contains a few basic notebooks to learn exploring, manipulating and measuring biomedical image data.
By the end of these notebooks, you should feel more comfortable with:
- Competence in exploring, manipulating, and analyzing biomedical image data using Python and relevant libraries.
- Proficiency in image processing techniques including segmentation, filtering, and measurement applicable in biomedical contexts.
- Understanding of how to assess and compare biomedical images for various purposes, including disease evaluation and structural analysis.
📣 Current announcements
In this skill track, the notebooks build on each other. Therefore, complete them in the order given!
❗ Course requirements
- you should have understood the basic concepts of Python, otherwise have another look at the Introductory notebooks
- in this notebook we use data from open-source databases (the references are at the bottom of the page); in Google Colab the data is loaded automatically at the beginning of the notebook
- have a look at the theoretical basics before you start with the notebooks
📒 Syllabus
- Basic concepts of Exploration
- Basic concepts of Image Comparison
- Basic concepts of Masks and Filters
- Basic concepts of Measurements
📝 Theoretical basics
For some information on the topic, take a look at some Basics.
☝️ References
In this skill track, you’ll work with different Open-Source datasets:
- CT scan from The Cancer Imaging Archive
- Hand radiograph from a 2017 Radiological Society of North America competition
- MR imaging data from the Sunnybrook Cardiac Dataset [1]
- MRI DICOM data set head of a normal male human [2] and
- Open Access Series of Imaging Studies Oasis [3]
[1] Radau, P. et al., Evaluation Framework for Algorithms Segmenting Short Axis Cardiac MRI, The MIDAS Journal – Cardiac MR Left Ventricle Segmentation Challenge, http://hdl.handle.net/10380/3070
[2] Lionheart, W. R. B. et al. (2015), An MRI DICOM data set of the head of a normal male human aged 52 [Data set], Zenodo, https://doi.org/10.5281/zenodo.16956
[3] Marcus, DS et al., Open Access Series of Imaging Studies (OASIS): Cross-Sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults, Journal of Cognitive Neuroscience, 19, 1498-1507. doi: 10.1162/jocn.2007.19.9.1498