MPHY0030: Programming Foundations for Medical Image Analysis

UCL Module | MPBE | UCL Moodle Page

Term 1 (Autumn), Academic Year 2024-25

Yipeng Hu | [email protected] | Lead and lecturer
Shaheer U Saeed | [email protected] | lecturer and tutor
Yipei Wang | [email protected] | tutor
Xiangcen Wu | [email protected] | tutor
Yunzhe Li | [email protected] | tutor

1. Development environment

There is no requirement, in tutorials or assessed coursework, for what the development environment or tools that need to be used. However, technical support from this module is available for the setups detailed in the following documents, under docs folder.

• Supported development environment for Python

Python environment

The tutorials require a few dependencies, numpy, scipy, matplotlib. Individual tutorials may also require other libraries which will be specified in the readme.md in individual tutorial folders (see links below).

Miniconda or Anaconda is recommended to set up the Python development environment.

conda create --name mphy0030 numpy scipy matplotlib 

2. Python refresher course

This mini-course has two parts, Python programming and scientific computing, by Shaheer Saeed. Materials can also be found in the tutorials folder.

3. Tutorials

These are the tutorials under the tutorials folder using Python, with optionally assessed questions.

MATLAB is a proprietary multi-paradigm programming language and numerical computing environment developed by MathWorks. Some tutorials are also additionally with MATLAB code for those who have relevant experience.

Image filtering 3d

Efficient high-dimensional image filtering
Tutorial

Maximum intensity projection

Inverting computerised tomography to obtain x-ray images
Tutorial

Iterative closest point

A point set registration algorithm, iterative closest point (ICP)
Tutorial

Augmented reality on medical images

Display graphics overlaid with 3d medical imges
Tutorial

*Legacy materials

The legacy folder contains several tutorials used in previous years for expanding knowledge of the students who are interested in. For example:

3DSlicer: Open-Source Medical Image Computing

by Zachary Baum The demo for guest lecture "Use existing open-source for visualizations in Jupyter Notebooks" Tutorial

Parallel computing using PyTorch

by Qianye Yang
The demo for guest lecture "Parallel computing using PyTorch"
Tutorial

Spatial transformations

by Adria Casamitjana
The demo for guest lecture "Spatial transformations and resampling"
Tutorial