KU Leuven
APPLIED IMAGE PROCESSING AND ANALYSIS FOR VIRTUAL 3D HISTOLOGY
KU Leuven
KU Leuven is an autonomous university. It was founded in 1425. It was born of and has grown within the Catholic tradition.
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JOB DETAILS
Published: 7 days ago
Application deadline: Aug 31
Location: Leuven, Belgium
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APPLIED IMAGE PROCESSING AND ANALYSIS FOR VIRTUAL 3D HISTOLOGY

(ref. BAP-2018-395)

We offer a joint PhD between the KU Leuven and the Université Catholique de Louvain linked to the Materials Performance and Non-destructive Testing group of the Dept. Materials Engineering (KU Leuven – Belgium), the Molecular Design and Synthesis group of the Department of Chemistry (KU Leuven – Belgium) and the Biomechanics lab of the Institute of Mechanics, Materials and Civil Engineering (UCLouvain – Belgium). https://uclouvain.be/en/research-institutes/immc https://www.mtm.kuleuven.be/English/ https://chem.kuleuven.be/en

Project

Biological tissues have a spatial heterogeneity. Thus, 2D measurements like standard histomorphometry only partially reveal their full 3D morphology. X-ray microfocus computed tomography (XCT) is an imaging technique that allows acquiring a 3D image of the microstructure of materials/tissues visualizing not only their composition, but also their internal architecture at the microscopic level in a non-destructive way. When combined with tissue-specific contrast agents for soft tissues, it allows for 3D multi-tissue imaging (i.e. contrast-enhanced XCT or CE-CT). The important advantage of this novel methodology is that the structure and organization of different sub-tissues within one macro-tissue can be analysed in 3D. As pioneers in the field, using this novel multi-tissue imaging methodology, we aim to set the stage for a new era of virtual 3D histology of biological tissues.

As XCT images are typically greyscale images, standard image analysis is normally based on grey-scale segmentation, which relies on the differences of the intensity values between the tissues of interest. However, due to the presence of multiple tissues in the CE-CT images, these differences of the intensity values are often not sufficient for a straightforward segmentation of the tissues of interest. Therefore, the aim of this PhD project is to develop advanced segmentation and analysis tools that use for example texture recognition, cluster analysis or machine learning rather than pure grey-scale-based approaches. The PhD student will not only develop his/her own algorithms, but will have to look for existing solutions in other field of application and optimize these techniques specifically for CE-CT.

Profile

The candidate will join our interdisciplinary research team and will be responsible for (i) testing and validating CE-CT contrast agents, developed within the consortium, for virtual 3D histology of different biological tissues and (ii) developing and validating advanced image processing and analysis approaches for segmentation and analysis of the multi-tissue CE-CT images. Therefore, the candidate should have:

  • an MSc degree in Biomedical Engineering, Bio-Engineering or a related discipline, and should be graduated with at least distinction 
  • a strong interest and background in image processing and analysis tools, such as machine learning and texture analysis
  • an equally strong interest to perform experimental work (CE-CT imaging and testing of novel contrast agents)
  • good communication skills in English, both spoken and written
  • the capacity to act independently and to take initiatives
  • an interest in bridging the gap between the engineering and biological/biomedical sciences
Offer

We offer:

  • a joint PhD degree (between the KU Leuven as main university and the UCLouvain - https://www.kuleuven.be/research/phd/joint_degree/)
  • a young and dynamic interdisciplinary research environment
  • a framework to boost intensive collaborations between the two universities involved, but also with other partners at the national and European level
Interested?

Interested applicants may contact Prof. Greet Kerckhofs (greet.kerckhofs@uclouvain.be) with “PhD in applied Image Processing and Analysis” in the topic of the email. Your application should include a motivation letter, a full CV with photograph, (potentially) a list of publications, academic transcripts, and contact information of at least two references.

You can apply for this job no later than August 31, 2018 via the online application tool

KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.

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