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Title: Evaluation of lung volume estimation methods from foetal MRI data
Offered to:
401 - Individual Project MEng
401I - Individual Project MEng - EIE
401J - Computing Individual Project MEng - JMC
301 - Individual Project BEng
301I - Individual Project BEng - EIE
301J - Individual Project BEng - JMC
Recently, several novel Magnetic Resonance Imaging (MRI) sequences have been developed to scan a foetuses inside the womb. We have developed a method to estimate the lung volume of foetuses in-utero exactly. However, the importance of this value for the clinical practise is still unknown. The aim of this project is to compare the state-of-the-art methods to estimate the foetal lung volume with our novel methods and to perform statistical analysis of the results. The question to be answered is which of the investigated methods is most reliable for a clinical use as a biomarker for the baby's health.
The key objectives of this project are therefore:
- to perform a professional literature research,
- to facilitate the currently available foetal lung evaluation pipelines,
- to generate measured lung volume data for a database of foetal datasets,
- to derive statistical analysis of the variation of the results and to compare the outcome with expected values for a given foetal age.
The project should be implemented in Matlab, C/C++, or R running on a Desktop PC. Reasonable good programming and scripting skills and experience in image processing and machine learning are desirable.
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Title: Uncertainty visualization for foetal MRI 3D super-resolution
Offered to:
301 - Individual Project BEng
301I - Individual Project BEng - EIE
301J - Individual Project BEng - JMC
401 - Individual Project MEng
401I - Individual Project MEng - EIE
401J - Computing Individual Project MEng - JMC
Description:
Super-resolution reconstruction of the foetal brain got recently possible through the development of novel Magnetic Resonance Imaging (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 perform a professional literature research,
- 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. Good programming skills and experience in image processing and machine learning are desirable.
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Title: The MRI 3D foetus detector
Offered to:
301 - Individual Project BEng
301I - Individual Project BEng - EIE
301J - Individual Project BEng - JMC
401 - Individual Project MEng
401I - Individual Project MEng - EIE
401J - Computing Individual Project MEng - JMC
Description:
Recently, several novel Magnetic Resonance Imaging (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 perform a professional literature research,
- to implement state-of-the-art and novel object detection algorithms and to train and evaluate their performance on the individual slices of a 3D datasets,
- to compare the performance of different image analysis approaches for this specific task,
- to use machine learning for an automatically generated probability map and to visualize the likelihood of foetal tissue.
The project should be implemented in Matlab or C/C++ running on a Desktop PC. Good programming skills and experience in image processing and machine learning are desirable.
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Title: Machine learning for the automatic evaluation of 4D foetal in-utero data sets
Offered to:
401 - Individual Project MEng
401I - Individual Project MEng - EIE
401J - Computing Individual Project MEng - JMC
301 - Individual Project BEng
301I - Individual Project BEng - EIE
301J - Individual Project BEng - JMC
Description:
Currently, the face of prenatal diagnostics is changing rapidly. Novel Magnetic Resonance Imaging (MRI) sequences allow making 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 perform a professional literature research,
- to extend or re-implement state-of-the-art motion estimation algorithms for fully automatic limb detection using machine learning methods,
- to compare the performance of different image analysis approaches for the specific task of foetal 4D imaging,
- to deploy the developed method as a useful tool for bioengineering research at Imperial.
The project should be implemented in Matlab or C/C++ running on a Desktop PC. Good programming skills and experience in image processing and machine learning are desirable.
This project is especially rewarding because useful approaches are quickly integrated into the biological/clinical research practise. Therefore, it is likely that the successful candidate will see her or his name mentioned together with ground-breaking results about the human development!
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These projects are especially rewarding because useful approaches are quickly integrated into the biological/clinical research practise. Therefore, it is likely that the successful candidate will see her or his name mentioned together with ground-breaking results about the human development!
