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29 mars 2017

3D imaging: Cerebral Vasculature Detection

Catégorie : Post-doctorant

Post-doc position on 3D medical imaging: vascular bifurcation detection, blood vessels tortuosity measurements.


Context of the Project

In the context of a collaboration between two labs of the University Hospital: UMR_S 1087 (Institut du Thorax) and UMR 1229 (RMeS, Regenerative Medicine and Skeleton), we are interested in exploring the brain vascularisation. Effectively, in order to prevent cerebrovascular accidents, it is important to study the blood flow at the major bifurcations of the cerebral vasculature (we mostly focus on the major arteries surrounding the Circle of Willis).

At The University of Nantes, we are thus looking for a young researcher (Ph.D degree required), who may be interested in pursuing a post-doc (at least one year) on medical Imaging.

Topic description

The project focuses on 3D vascular tree detection (mouse µScan, and human TOF&IRM imaging ), and more particularly vascular bifurcation detection, intracranial aneurysms detection, blog vessels tortuosity measurement, etc.

The post-doc fellow will be employed by the INSERM for one year, he will join the RMeS lab at the University hospital in Nantes.

The applicant will study various ways to segment the vascular tree and detect the bifurcations. Once the bifurcations are located, several measures will have to be collected, such as the three diameters constituting the bifurcation, the angle formed by the bifurcation, the distance between two consecutive bifurcations. Concerning the tortuosity measurements, the distance between the shortest path and the actual position of the blood vessel bounded by two consecutive bifurcations will be computed. The project can be achieved using various methods, machine learning can be considered, as well as mathematical morphology tools.


PhD degree in computer science, image processing or applied mathematics mandatory.

A couple of years of postdoctoral experience would be a plus.

Good oral and written English skills expected.

Good programming skills are required (either Python or C/C++)


Candidates should send a cover letter along with an extended curriculum vitae to Florent Autrusseau (Florent.Autrusseau@univ-nantes.fr)

The post-doctoral position should start in May/June 2017.

The salary depends on the applicant’s experience, and should be between 2 500 and 2 900 € (gross monthly salary)


Zhao, M., & Hamarneh, G. (2014). Bifurcation Detection in 3D Vascular Images Using Novel Features and Random Forest. In IEEE International Symposium on Biomedical Imaging (IEEE ISBI) (pp. 421–424).

Brozio, M., Gorbunova, V., Godenschwager, C., Beck, T., & Bernhardt, D. (2012). Fast Automatic Algorithm for Bifurcation Detection in Vascular CTA Scans. Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142U (February 23, 2012), 8314(0), 83142U–83142U–9. http://doi.org/10.1117/12.911329

Fotin, S. V., Reeves, A. P., Biancardi, A. M., Yankelevitz, D. F., & Henschke, C. I. (2010). Standard Moments Based Vessel Bifurcation Filter for Computer-Aided Detection of Pulmonary Nodules. SPIE Medical Imaging, 7624, 1–10. http://doi.org/10.1117/12.844516

Baboiu, D. M., & Hamarneh, G. (2012). Vascular bifurcation detection in scale-space. Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis, 41–46. http://doi.org/10.1109/MMBIA.2012.6164767

Robben, D., Türetken, E., Sunaert, S., Thijs, V., Wilms, G., Fua, P., … Suetens, P. (2016). Simultaneous segmentation and anatomical labeling of the cerebral vasculature. Medical Image Analysis, 32, 201–215. http://doi.org/10.1016/j.media.2016.03.006

Annunziata, R., Kheirkhah, A., Aggarwal, S., Hamrah, P., Trucco, E., Image, B., … England, N. (2016). A fully automated tortuosity quantification system with application to corneal nerve fibres in confocal microscopy images. Medical Image Analysis, 32, 216–232. http://doi.org/10.1016/j.media.2016.04.006

Lidayová, K., Frimmel, H., Wang, C., Bengtsson, E., & Smedby, Ö. (2016). Fast vascular skeleton extraction algorithm. Pattern Recognition Letters, 76, 67–75. http://doi.org/10.1016/j.patrec.2015.06.024

KUmar, R. P., Albregtsen, F., Reimers, M., Edwin, B., Langø, T., & Elle, O. J. (2015). Three-Dimensional Blood Vessel Segmentation and Centerline Extraction based on Two-Dimensional Cross-Section Analysis. Annals of Biomedical Engineering, 43(5), 1223–1234. http://doi.org/10.1007/s10439-014-1184-4

Macía, I., Graña, M., & Paloc, C. (2012). Knowledge management in image-based analysis of blood vessel structures. Knowledge and Information Systems, 30(2), 457–491. http://doi.org/10.1007/s10115-010-0377-x


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