PhD student Segmentation of MRI using machine learning
PhD student Segmentation of MRI using machine learning
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Job description
The PhD project
Magnetic Resonance Imaging (MRI) is a powerful technique to visualize and quantify human anatomy and function, with the ultimate goal to support diagnosis and treatment of disease. In the early days of MRI, clinical users primarily interpreted the images visually, but nowadays more and more automatic image segmentation algorithms are used to derive the essential information.
The design of accurate and robust MR image segmentation algorithms is a very challenging task, due to the highly varying appearance of anatomical structure in the MR images. This variation can be caused by differences in the scan protocol (scanner parameter settings), but also by patient variations (e.g. differences in weight) and by scanner software and hardware variations.
The development, optimization, and validation of image segmentation algorithms is a tedious and time-consuming process. Especially novel model-based methods that use machine learning approaches, such as deep learning, require large training sets of annotated data. There is a clear need to develop MRI segmentation methods that are as much as possible insensitive to the specific MR image contrast (T1-weighted, T2-weighthed, etc.) and to patient and scanner variations. The development of such algorithms is the focus of this PhD project. Their availability would significantly reduce training and validation efforts and broaden the applicability in clinical practice.
The PhD research is funded by the highly prestigious and competitive Marie Curie Innovative Training Networks (ITN) fellowship program, in a project called 'Open Ground Truth Training Network (openGTN)', see the website openGTN.eu. You will work closely together with 2 other PhD researchers in openGTN, who will focus on generating ample simulated MR images of the brain, spine and heart, with ground truth segmentations, supplying you with data for training and testing of your algorithms.
The team
You will become part of the Medical Image Analysis group at the Department of Biomedical Engineering, Eindhoven University of Technology. The group consists of around 20 researchers, all working on the development of image analysis methodology as well as clinical applications. Collaborations with various hospitals and with industry ensure involvement of clinical experts, and hence a focus on clinically relevant topics. Through various (formal and less formal) meetings, the members of the group share ideas and work together in an enthusiastic and encouraging atmosphere. More information on the group can be found at www.tue.nl/image .
The Department of Biomedical Engineering consists of a large number of research groups, clustered around three themes: (i) Chemical Biology, (ii) Biomechanics and Tissue Engineering and (iii) Biomedical Imaging and Modeling. The department offers research driven Bachelor and Masters programmes. The department has more than 1,000 students and up to 200 tenured and non-tenured employees.
For this Marie Curie ITN project, you will spend 50% of your time at Philips Research Hamburg, Germany (in the first three years), and you will also work for extended secondments (short externships) at the University Medical Center Utrecht, The Netherlands, Philips Healthcare, Best, The Netherlands, King's College London, United Kingdom, Deutsches Zentrum für Neurodegeneratieve Erkrankungen, Bonn, Germany, and Klinikum Rechts der Isar / Technical University, München, Germany. You will be academically supervised by prof. dr. Marcel Breeuwer (professor at TU/e and Principal Scientist at Philips Healthcare, project leader of openGTN) and prof. dr. Josien Pluim (head of the Medical Image Analysis department).
Your role and responsibilities
Your research will focus on inventing, implementing, optimizing and validating image analysis algorithms that are as much as possible independent of the applied specific MRI scanner and acquisition method. Your will publish your results at scientific conferences and in peer-reviewed scientific journals.
Magnetic Resonance Imaging (MRI) is a powerful technique to visualize and quantify human anatomy and function, with the ultimate goal to support diagnosis and treatment of disease. In the early days of MRI, clinical users primarily interpreted the images visually, but nowadays more and more automatic image segmentation algorithms are used to derive the essential information.
The design of accurate and robust MR image segmentation algorithms is a very challenging task, due to the highly varying appearance of anatomical structure in the MR images. This variation can be caused by differences in the scan protocol (scanner parameter settings), but also by patient variations (e.g. differences in weight) and by scanner software and hardware variations.
The development, optimization, and validation of image segmentation algorithms is a tedious and time-consuming process. Especially novel model-based methods that use machine learning approaches, such as deep learning, require large training sets of annotated data. There is a clear need to develop MRI segmentation methods that are as much as possible insensitive to the specific MR image contrast (T1-weighted, T2-weighthed, etc.) and to patient and scanner variations. The development of such algorithms is the focus of this PhD project. Their availability would significantly reduce training and validation efforts and broaden the applicability in clinical practice.
The PhD research is funded by the highly prestigious and competitive Marie Curie Innovative Training Networks (ITN) fellowship program, in a project called 'Open Ground Truth Training Network (openGTN)', see the website openGTN.eu. You will work closely together with 2 other PhD researchers in openGTN, who will focus on generating ample simulated MR images of the brain, spine and heart, with ground truth segmentations, supplying you with data for training and testing of your algorithms.
The team
You will become part of the Medical Image Analysis group at the Department of Biomedical Engineering, Eindhoven University of Technology. The group consists of around 20 researchers, all working on the development of image analysis methodology as well as clinical applications. Collaborations with various hospitals and with industry ensure involvement of clinical experts, and hence a focus on clinically relevant topics. Through various (formal and less formal) meetings, the members of the group share ideas and work together in an enthusiastic and encouraging atmosphere. More information on the group can be found at www.tue.nl/image .
The Department of Biomedical Engineering consists of a large number of research groups, clustered around three themes: (i) Chemical Biology, (ii) Biomechanics and Tissue Engineering and (iii) Biomedical Imaging and Modeling. The department offers research driven Bachelor and Masters programmes. The department has more than 1,000 students and up to 200 tenured and non-tenured employees.
For this Marie Curie ITN project, you will spend 50% of your time at Philips Research Hamburg, Germany (in the first three years), and you will also work for extended secondments (short externships) at the University Medical Center Utrecht, The Netherlands, Philips Healthcare, Best, The Netherlands, King's College London, United Kingdom, Deutsches Zentrum für Neurodegeneratieve Erkrankungen, Bonn, Germany, and Klinikum Rechts der Isar / Technical University, München, Germany. You will be academically supervised by prof. dr. Marcel Breeuwer (professor at TU/e and Principal Scientist at Philips Healthcare, project leader of openGTN) and prof. dr. Josien Pluim (head of the Medical Image Analysis department).
Your role and responsibilities
Your research will focus on inventing, implementing, optimizing and validating image analysis algorithms that are as much as possible independent of the applied specific MRI scanner and acquisition method. Your will publish your results at scientific conferences and in peer-reviewed scientific journals.
Specifications
- max. 38 hours per week
- Eindhoven View on Google Maps
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Eindhoven University of Technology (TU/e)
Requirements
Applicants should have a Master's degree in Electrical Engineering, Computer Science, Applied Physics, Applied Mathematics or Biomedical Engineering. Applicants with prior experience in MRI and medical image analysis will be given highest preference, as will candidates with ample experience in software design and implementation. Candidates should be able to work independently as well as in a team and be highly skilled in written and spoken English.
Candidates will be required to meet the Marie Curie Early Stage Researcher eligibility criteria. In particular, at the time of recruitment by the host organisation, candidates must be in the first four years (full-time equivalent research experience) of their research careers and not yet have been awarded a doctoral degree. This is measured from the date when they obtained the degree which would formally entitle them to embark on a doctorate. They are required to undertake trans-national mobility (i.e. move from one country to another) when taking up their appointment. At the time of recruitment by the host organisation, researchers must not have resided or carried out their main activity (work, studies, etc.) in The Netherlands for more than 12 months in the 3 years immediately prior to the start date. Short stays such as holidays and/or compulsory national service are not taken into account.
Candidates will be required to meet the Marie Curie Early Stage Researcher eligibility criteria. In particular, at the time of recruitment by the host organisation, candidates must be in the first four years (full-time equivalent research experience) of their research careers and not yet have been awarded a doctoral degree. This is measured from the date when they obtained the degree which would formally entitle them to embark on a doctorate. They are required to undertake trans-national mobility (i.e. move from one country to another) when taking up their appointment. At the time of recruitment by the host organisation, researchers must not have resided or carried out their main activity (work, studies, etc.) in The Netherlands for more than 12 months in the 3 years immediately prior to the start date. Short stays such as holidays and/or compulsory national service are not taken into account.
Conditions of employment
Working conditions
This post is available from 1st July 2018 or as soon as possible thereafter, but not later than 1 September 2018, and will be offered a full-time appointment for four years at Eindhoven University of Technology.
We offer you:An exciting job in a dynamic work environment The possibility to present your work at international conferences. A full time appointment for four years at Eindhoven University of Technology (www.tue.nl/en) The salary is in accordance with the Collective Labour Agreement of the Dutch Universities, increasing from € 2,222 per month initially, to € 2,840 in the fourth year. An attractive package of fringe benefits, including end-of-year bonus (8.3% in December), an extra holiday allowance (8% in May), moving expenses and excellent sports facilities.
This post is available from 1st July 2018 or as soon as possible thereafter, but not later than 1 September 2018, and will be offered a full-time appointment for four years at Eindhoven University of Technology.
We offer you:
