PhD position: Tumour Angiogenesis Assesment using Marked Point Processes


Position available from:

1er décembre 2015 (date limite de soumission: 15 novembre 2015)


The hosting French University is Sorbonne Universities, University Pierre and Marie CURIE (UPMC Univ Paris 06), which is currently the largest scientific and medical complex in France, UPMC is involved in every research domain and has an academic reputation of the very highest order, as attested by the numerous awards and prizes that its researchers regularly receive and the many international collaborations it has formed in countries across the five continents.

The laboratory concerned by this study in UPMC is the LIB (Laboratoire d’Imagerie Biomédicale : INSERM – French National Institute for Health and Medical Research /CNRS – National Centre for Scientific Research). The LIB specializes in the fundamental research and applications of biomedical imaging methods for morphologic, functional and molecular exploration of small animals and humans. The main foci of investigation are are among the twenty-first century public health priorities: bone, cancer, cardiovascular and neurological diseases. We develop new non-invasive diagnosis and treatment methodologies in our main field of investigation, including ultrasound, MRI, CT and SPECT-PET. The team of LIB involved in this PhD study is the team ITD: Imaging and Therapy Development: nanostructures to humans – applied to cancer management.

The Singaporean host institute of the research is Bioinformatics Institute, (BII), Agency for Science, Technology and Research (A*STAR), Singapore. BII was set up by the A*STAR in 2001, located in Biopolis, Singapore. BII is conceived as a computational biology research and postgraduate training institute as well as a national resource centre in bioinformatics within the Biomedical Research Council (BMRC) of A*STAR.



The research project aims to build a closed-loop pipeline for the assessment of tumour angiogenesis, with automated quantitative analysis using integrated data from various modalities, including dynamic contrast enhanced magnetic resonance images (Multi-parametric Functional MRI) of tumours, sequential series of tumour immunohistochemistry (IHC) slice images and data from tumour xenograft model under various candidates of treatments. Results of analysis will be used for further evidence-based medical practices. We aim to collaborate with companies to commercialize and translate our technology to make it a standard platform for quantitative analysis of tumour angiogenesis.

Renal tumour samples from both human and xenograft models receiving anti-angiogenesis therapy (bevacizumab, sunitinib and sorafenib) will be acquired. The tumour will be sliced sequentially and IHC will be applied, subsequently imaged using a microscope. Images of the slices will be reconstructing the original 3D structure using advanced deformable image registration methods. Prior to tumour extraction, the relevant multi-parametric Functional MRI image of the tumour will be acquired to extract information on blood flow, blood volume, vascular permeability and extracellular fluid. Data mining methods will be developed to correlate data from all modalities (i.e. IHC, MRI and xenograft) to understand the microvascular network and its relation to cancer development.

In this project, a certain number of cancer patients will be recruited. The patients will be diagnosed, and the tumours will be extracted. A part of an extracted tumour will be sequentially sliced and processed in regular IHC. The 3D model of the tumours will be established using the scanned images of the slices.

The technology we envisage to use for this sutdy is the Marked Point Process (see [1,2,3]), which will need to be adapted to the environment and the needs of this project. The MPP has the advantage to be generic and highy paralelizable, which is an important advantage for 3D and huge databases we are supposed to deal with in the near future.


  1. Educational Qualifications: MSc or equivalent in Computer Science
  2.  Essential:
    1. Strong background in pattern recognition, computer vision and machine learning, demonstrated by the successful completion of courses.
    2. Good oral written and presentation skills.
    3.  Experience of managing a research project and setting research targets.
    4. Well-organised, attention to detail and ability to meet deadlines.
    5. Ability to think logically, create solutions and make informed decisions.
  3. Desirable:
    1. Excellent writing skills, including word processing such as MS-WORD or LaTeX.
    2. Excellent IT skills, including programming in C/C++ or Java, Python or Matlab, etc.
  4. Personal:
    1. Fluency and clarity in spoken English.
    2. Good written English.
    3. Ability to work collaboratively as part of a team.
    4. Commitment to high quality research.

Functions of the job (PhD) position include :

  1. 3D model reconstruction using 2D histopathological slides.
  2. Blood vessel network reconstruction from the 3D model.
  3. Quantitative analysis on the micro structure for angiogenesis, including:
    1. Quantitative analysis on micro vascular network of tumour, including blood vessel volume, density, thickness, branches and length.
    2. Pericite detection and quantitative analysis.
  4. Growing factor analysis of tumour angiogenesis.
  5. Development of the relevant methods of computer vision and machine learning.
  6. Correlation between various modalities and histopathology computed parameters, including fMRI and xenotransplantation.


NB : PDF version available at :

  1. Sreetama Basu, Daniel Racoceanu, Reconstructing Neural Morphology from Microscopiy Stacks Using Fast Marching, IEEE ICIP 2014 – International Conference on Image Processing, Paris, 27-30 Oct. 2014.
  2. Sreetama Basu, Daniel Racoceanu, Wei Tsang Ooi, Improved Marked Point Process Priors for Single Neurite Tracing, Pattern Recognition in Neuroimaging, PRNI 2014, Tübingen, Germany, 4-6 June 2014.
  3. Sreetama Basu et al., A Stochastic Model for Automatic Extraction of 3D Neuronal Morphology, MICCAI 2013 – International Conference on Medical Image Computing and Computer Assisted Intervention, Japan, Osaka, Oct 2013.


Contrat doctoral Université Pierre et Marie Curie, cofinancé avec A*STAR Singapour


ENCADREMENT : Prof. Daniel RACOCEANU (UPMC Univ Paris 06, Paris, LIB, France), Dr. HUANG Chao Hui (BII, A-STAR, IPAL, Singapore), A/Prof. LEE Hwee Kuan (BII, A-STAR, IPAL, Singapore).

CONTACT : Daniel Racoceanu : ;

Where the PhD will take place:

LIB Pitié-Salpêtrière, Paris (main location), A*STAR BII Singapour (part time)