I am a PhD student at the Institute of Biomedical Engineering at Polytechnique Montreal. My researches are focused on the development of new neuroimaging methods for characterizing the macro- and micro-structure of the central nervous system (brain and spinal cord).
I am currently working on new image processing method specialized for multiparametric MRI data of the spinal cord. Particularly, I am developping a new generic MRI template of the spinal cord and brainstem, that enables multicentric studies of large datasets of patients with neurodegenerative diseases.
I have developped an automatic segmentation method for the spinal cord in MR images. It exist only a few methods in the literature which are fully automatic but they are generally adapted for only one image acquisition sequence (e.g. T1-weighted images) and for a particular field of view (FOV). The objective of my project is to develop an automatic segmentation method of the spinal cord which can work on different type of images, independently of the FOV.
We have developed a method using propagated deformable model in a multi-resolution fashion and coupled with a contrast-adaptive mechanism. This method has been validated on 15 healthy subjects and 2 patients with spinal cord injuries by comparing with manual segmentation. High accuracy has been demonstrated (Dice coefficients = 0.9 +- 0.3).
This automatic segmentation process allows the conduction of large group studies, for example in the assessment of spinal cord atrophy, which has been correlated with multiple sclerosis. Our program is freely available on http://sourceforge.net/projects/spinalcordtoolbox/
benjamin.de-leener "at" polymtl.ca
De Leener B, Cohen-Adad J, Kadoury S. Automatic segmentation of the spinal cord and spinal canal coupled with vertebral labeling. IEEE Transactions on Medical Imaging 2015; (in press)
Fonov VS, Le Troter A, Taso M, De Leener B, Leveque G, Benhamou M, Sdika M, Benali H, Pradat PF, Collins DL, Callot V, Cohen-Adad J. Framework for integrated MRI average of the spinal cord white and gray matter: The MNI-Poly-AMU template. Neuroimage 2014;102P2:817-827.
De Leener B, Kadoury S, Cohen-Adad J. (2014) “Robust, accurate and fast automatic segmentation of the spinal cord”, Neuroimage, 98, 528-536. doi:10.1016/j.neuroimage.2014.04.051
Mertens, B., De Leener, B., Debeir, O., Beumier, C., Lambert, P., Delchambre, A. (2014). “Robust Structured Light Pattern for Use with a Spatial Light Modulator in 3-D Endoscopy.” International Journal of Optomechatronics, 7(2), 105–121. doi: 10.1080/15599612.2013.785041
De Leener B. ; Cohen-Adad J. and Kadoury S., “Automatic 3D segmentation of spinal cord MRI using propagated deformable models”, Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90343R (March 21, 2014); doi:10.1117/12.2043183
Mertens, B., De Leener, B., Debeir, O., Beumier, C., Lambert, P., Delchambre, A., “Robust structured light pattern for use with a hologram in 3D endoscopy,” Optomechatronic Technologies (ISOT), 2012 International Symposium on, 1-6, 29-31 Oct. 2012. doi: 10.1109/ISOT.2012.6403279