New student projects available

I'm offering a few very interesting student projects (MSc, Mres) this year for students of Imperial College London. If you're interested log in at https://cate.doc.ic.ac.uk and select one of my projects:

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Machine learning for the automatic evaluation of foetal movements in-utero

Currently, the face of prenatal diagnostics is changing rapidly. Novel MRI sequences allow to make videos of the foetal movements inside the womb with a large field of view. Thereby, the assessment of motor function is an essential component of neurology examinations. Recent research shows evidence that it is possible to assess the healthiness of a foetus by observing the kind and extend of movements. An automatic pre-classification in healthy and abnormal foetal behaviour would form a valuable tool for the clinical practise.

The key objectives of this project are therefore:
- to implement state-of-the-art motion estimation algorithms, such as optical flow, using existing libraries and to compare their performance.
- to derive significant features from the motion analysis and to use machine learning techniques to distinguish between healthy and abnormal foetal behaviour.

Bonus objective:
The datasets show a combination of maternal movements and foetal movements, which are not directly related. Therefore, a desirable additional objective would be to extract movement features that are only related to the foetus.

The project should be implemented in Matlab or C/C++ running on a Desktop PC. Excellent programming skills and experience in image processing and machine learning are desirable.

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The MRI foetus detector

Recently, several novel MRI sequences have been developed to scan a foetuses inside the womb. The resulting 3D scans show, besides the foetus, a significant amount of maternal tissue and are also subject to movement artefacts caused by the foetus. An automatic evaluation of the foetal organs would be desirable but is currently difficult because of the large amount of background information. The aim of this project is to suppress the maternal background information and to easy subsequent processing of pre-natal foetal MRI datasets.

The key objectives of this project are therefore:
- to implement state-of-the-art object detection algorithms, using existing libraries, and to train and evaluate their performance on the individual slices of the 3D datasets.
- to use machine learning for an automatically generated probability map, showing the likelihood of  foetal tissue.

The project should be implemented in Matlab or C/C++ running on a Desktop PC. Excellent programming skills and experience in image processing and machine learning are desirable.

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Uncertainty visualization of foetal brain super-resolution reconstructions

Super-resolution reconstruction of the foetal brain got recently possible through the development of novel MRI sequences for the womb. Thereby, several low resolution image stacks of the foetus are acquired in overlapping but different directions. These image stacks can subsequently be used to reconstruct a high resolution volume of the desired region, usually the foetal brain. However, the resulting images show errors that are not directly noticeable. Therefore, a useful clinical extension of this approach would be to develop a tool that is able to visualize the errors, which occur during the reconstruction process.

The key objectives of this project are therefore:
- to extend an existing framework for the reconstruction of foetal MRI data, so that additional information about the uncertainty of the calculated intensity values are generated.
- to develop a tool for the visualization of these errors. Thereby, the uncertainty information should not distract the user from the anatomical information and state-of-the-art uncertainty visualization methods should be used.

The project should be implemented in  C/C++ running on a Desktop PC. The visualization can be integrated into existing visualization software. Excellent programming skills and experience in image processing and machine learning are desirable.
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30s IEEE VIS teaser

We did a nice 30s teaser for our talk at IEEE VIS 2013. You can watch it here: