( with ad hoc curriculum* on COVID-19 and "outbreak science" )
[Work in progress ver. 2021-05-10]
This is the repository for the course BMED360 given at the Department of Biomedicine
Biomedicine covers those areas of human biology, chemistry and medicine that seek to explain the factors behind health and disease at the molecular and cellular level. This information is applied in the development of better diagnostics and treatments.
In this repository you find presentations, documentation, code, and partly data for the course.
The goal of this course is to obtain theoretical and practical knowledge on functional and quantitative in vivo imaging in man and animal using magnetic resonance imaging (MRI) and computer-based image analysis. The focus is on brain imaging (perfusion, diffusion, permeability mapping) and structural and functional connectivity, but also examples from functional kidney imaging and (image-based) systems biology will be presented. A major objective is also to give insight about the importance of mathematical models and computations in analysis and understanding of complex physiological processes, and the need of cross-disciplinary collaborations.
*)
We plan to run BMED 360 (n students enrolled) starting Tuesday April 20 ("fully digitized") with a slightly modified curriculum, also addressing COVID-19 and "outbreak science" from a computional imaging and modeling perspective. There will be online slides, computer labs (code and data on GitHub, using Discussions for student interaction), assignments/challenges, digital MCQ, and a final digital oral exam from home (presenting yor personal project on Zoom - taking load from MittUiB, being mostly used for static information). Grading has been A-F, this semester we will use pass/nopass.
NOTE: This is work IN PROGRESS . . .
You will find more (static) information about this course at MittUiB
Follow the instructions at Setting up your system (setup.md) to get ready
- Display and functionality might differ between browsers - we recommend using Google Chrome on all platforms
The course is based on the Jupyter Notebook, a web-based framework for developing and presenting code-based projects (take a look at https://youtu.be/HW29067qVWk og https://youtu.be/2eCHD6f_phE for introductions to Jupyter Notebooks).
This repository is also available on Google Colaboratory (Colab): http://colab.research.google.com/github/computational-medicine/BMED360-2021. See also the individual Jupyter notebooks for execution on Colab - a hosted Jupyter notebook service that requires no setup to use, while providing free access to computing resources including GPUs.
Throughout the course you will work with notebooks that contain various material and programming tasks. We recommend that you make a copy of our notebooks before you are editing them. In this respect you might adopt the naming convention my_[name_of_notebook].ipynb. Remember also to start a new session with a git pull (things can have changed).
- Python, Numpy, Pandas, Matplotlib, Nibabel, Biopython and more: run through this notebook (
Test-Notebook/0.0-test-installation.ipynb) to check that your environment is OK.
LECTURES:
- Lec 0: Course overview; SW installation; Motivation: Can a biologist fix a radio? Lazebnic (2002); Why programming?; Why top-down? - teaching "the whole game" (see also https://computingskillsforbiologists.com)
- Lec 1: Introduction to modelling, MRI, and image processing; BROWSE through: [Tofts (2018) Ch. 1, Ch. 2, Ch. 17, Ch. 18; McRobbie (2017) Ch. 3, Ch. 4, Ch. 5, Ch. 8]
- Lec 2: Water diffusion, dMRI, and tissue microstructure - Part 1 [Tofts (2018) Ch. 8; McRobbie (2017) Ch. 18 pp. 303-310]. Video recording from the dMRI session: https://youtu.be/ZkVclYejv54
- Lec 3: Water diffusion, diffusion tensor imaging and beyond - Part 2 [Tofts (2018) Ch. 9; Westin (2002)]
- Lec 4: Blood perfusion and dynamic susceptibility contrast MRI (DSC-MRI) - Part 1 [Tofts (2003) Ch. 11; McRobbie (2017) Ch. 18 pp. 311-314]. Video recording from this session: https://youtu.be/h1AboyMq7Uw
- Lec 5: Blood perfusion, tracer kinetics, and deconvolution - Part 2 [Tofts (2003) Ch. 11]. Video recording from thos session: https://youtu.be/mTCs2MFxEzk
- Lec 6: Vascular permeability, compartment modelling, and T1w dynamic contrast-enhanced MRI (DCE-MRI) [Tofts (2018) Ch. 14; McRobbie (2017) Ch. 18 pp. 316-319; Measurement of Renal Perfusion and Filtration with MRI GitHub / slides]. Video recording from this session: https://youtu.be/X4zGyhid48U
- Lec 7: Brain connectivity assessed with aMRI, dMRI, fMRI and network (graph) theory [Fornito (2016) Ch. 1; Bassett (2018); McRobbie (2017) Ch. 18 pp. 319-325]. Video recording from this session: https://youtu.be/rAbNbpcUNdY
- Lec 8: Outbreak science and COVID-19 [README] (biology, epidemiology, geo-mapping, imaging) [covid-19-eda] [simulitis-outbreak]
LABS:
- Lab 0 (before the course): SW installation and Beginner's guides
- [README]
- [Lab0-00-begin-python-programming-language] (operators, control structures, lists, dictionaries, and more ...)
- [Lab0-01-jupyter-notebook-markdown-basics] (how to use the rich functionality of Markdown Cells in Jupyter notebooks)
- [Lab0-02-begin-pandas-basics] (likely the most important data analysis and manipulation tool for Python)
- [Lab0-03-begin-image-processing-basics] (relevant for the Midterm-Kiwi-Project)
- (and optionally, Prog4comp-SL-HPL-Extra for those who want to go deeper into numerical computing and simulations)
- Lab 1: Data analysis, image processing, and modelling in Python
- [README]
- [Lab1-00-get-mri-imc-data] (download MRI data and IMC (Imaging Mass Cytometry@FLUIDIGM) data used in this Lab1)
- [Lab1-01-mri-intro]
- [Lab1-02-mri-multispectral]
- [Lab1-03-mri-snr-cnr]
- [Lab1-04-imc-intro]
- Lab 2: Machine learning, multispectral imaging and tissue classification
- Lab 3: Processing of diffusion MRI (dMRI / DTI) [DIPY]
- [README]
- Lab 4: Processing of perfusion MRI (pMRI / DSC-MRI)
- [README]
- Lab 5: Vascular permeability mapping (DCE-MRI)
- [README]
- Lab 6: Network science and Graphs
- [README]
- [Lab6-01-Concepts-in-network-theory]
- [Lab6-02-Network-based-statistics]
- [Lab6-03-resting-state-fmri-expore]
- [Connectivity-fMRI] (brain connectivity and fMRI - concepts, software, and data)
ASSESSMENT / EXAM:
- Midterm assignment: [The KIWIfruit segmentation challenge]
- MCQ / Quiz: [README]
- Oral presentation of PROJECT: [README]
Previous course material
Q&A topics (from 2019 - still relevant) [gslides]
Syllabus at MittUiB (https://mitt.uib.no/courses/27782/assignments/syllabus)
