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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