Publications of Pierre-Louis Bazin

Kozlov, M., Kalloch, B., Horner, M., Bazin, P.-L., Weiskopf, N., & Möller, H. E. (2019). Patient-specific RF safety assessment in MRI: Progress in creating surface-based human head and shoulder models. In Brain and human body modeling: Computational human modeling at EMBC 2018 (pp. 245–282). Cham: Springer.

Bazin, P.-L., Shiee, N., Ellingsen, L. M., Jerry L. , P., & Dzung L. , P. (2011). Digital topology in brain image segmentation and registration. In A. S. El-Baz, R. Acharya U., & A. F. Laine (Eds.), Multi modality state-of-the-art medical image segmentation and registration methodologies (Vol. 1, pp. 339–375). New York: Springer.

Pham, D. L., & Bazin, P.-L. (2009). Unsupervised Tissue Classification. In Handbook of Medical Image Processing and Analysis (pp. 209–222). Elsevier.

Brammerloh, M., Kirilina, E., Alkemade, A., Bazin, P.-L., Jantzen, C., Jäger, C., … Weiskopf, N. (2021). Swallow tail sign and nigrosome 1: Close but not quite the same. In Proceedings of the ISMRM & SMRT Annual Meeting & Exhibition (Vol. 29). Retrieved from http://hdl.handle.net/21.11116/0000-000B-4AD1-6

Müller-Axt, C., Eichner, C., Kauffmann, L., Bazin, P.-L., Rusch, H., Morawski, M., … von Kriegstein, K. (2020). Relaxometry differences between magno- and parvocellular human LGN subdivisions revealed by in- and ex-vivo quantitative MRI. In Proceedings of the 28th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) (Vol. 28). Retrieved from http://hdl.handle.net/21.11116/0000-0006-D4B3-0

Kozlov, M., Bode, J., Bazin, P.-L., Kalloch, B., Weiskopf, N., & Möller, H. E. (2018). Building a high resolution surface-based human head and torso model for evaluation of specific absorption rates in MRI. In 2017 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS). doi:10.1109/COMCAS.2017.8244808

Thierbach, K., Bazin, P.-L., de Back, W., Gavriilidis, F., Kirilina, E., Jäger, C., … Scherf, N. (2018). Combining deep learning and active contours opens the way to robust, automated analysis of brain cytoarchitectonics. In Machine Learning in Medical Imaging 2018 (pp. 179–187). Cham: Springer.

Kozlov, M., Bode, J., Bazin, P.-L., Weiskopf, N., Möller, H. E., & Shajan, G. (2017). Comparison of 7T 16-channel dual-row transmit arrays. In Proceedings of the 2017 International Conference on Electromagnetics in Advanced Applications (ICEAA) (pp. 1264–1267).

Orasanu, E., Bazin, P.-L., Melbourne, A., Lorenzi, M., Lombaert, H., Robertson, N. J., … Ourselin, S. (2016). Longitudinal analysis of the preterm cortex using multi-modal spectral matching. In S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, & W. Wells (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part I (pp. 255–263). Cham: Spinger.

Kozlov, M., Bazin, P.-L., Weiskopf, N., & Möller, H. E. (2016). Effects of the geometry and size of the cerebrospinal fluid on MRI transmit and safety efficiencies at 300 MHz. In Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 2909–2912). Orlando, FL, USA.

Kozlov, M., Bazin, P.-L., Möller, H. E., & Weiskopf, N. (2016). Influence of cerebrospinal fluid on specific absorption rate generated by 300 MHz MRI transmit array. In Proceedings of 10th European Conference on Antennas and Propagation (EuCAP) (pp. 1–5). Piscataway: IEEE.

Bazin, P.-L., Plessis, V., Fan, A. P., Villringer, A., & Gauthier, C. (2016). Vessel segmentation from quantitative susceptibility maps for local oxygenation venography. In Proceedings of the IEEE 13th International Symposium on Biomedical Imaging (ISBI) (pp. 1135–1138). Piscataway: IEEE.

Dinse, J., Schäfer, A., Bazin, P.-L., & Weiskopf, N. (2016). Identifying intracortical partial voluming effects using cortical surface normals in quantitative MRI T1 maps sensitive to microstructure. In Bildverarbeitung für die Medizin 2016 (pp. 16–19).

Kim, S.-G., Stelzer, J., Bazin, P.-L., Viehweger, A., & Knösche, T. R. (2014). Group-wise analysis on myelination profiles of cerebral cortex using the second eigenvector of Laplace-Beltrami operator. In Proceedings of the 11th IEEE International Symposium on Biomedical Imaging (ISBI) (pp. 1007–1010). Beijing, China.

Dinse, J., Martin, P., Schäfer, A., Geyer, S., Turner, R., & Bazin, P.-L. (2013). Quantifying differences between primary cortical areas in humans based on laminar profiles in in vivo MRI data. In Bildverarbeitung für die Medizin 2013: Algorithmen, Systeme, Anwendungen; Proceedings des Workshops vom 3. bis 5. März 2013 in Heidelberg (pp. 39–44).

Dinse, J., Wähnert, M., Tardif, C., Schäfer, A., Geyer, S., Turner, R., & Bazin, P.-L. (2013). A histology-based model of quantitative T1 contrast for in-vivo cortical parcellation of high-resolution 7 Tesla brain MR images. In K. Mori, I. Sakuma, Y. Sato, C. Barillot, & N. Navab (Eds.), Medical image computing and computer-assisted intervention – MICCAI 2013 (pp. 51–58). Berlin: Springer.

Gorgolewski, K. J., Bazin, P.-L., Engen, H. G., & Margulies, D. S. (2013). Fifty Shades of Gray, Matter: Using Bayesian priors to improve the power of whole-brain voxel- and connexelwise inferences. In 2013 International Workshop on Pattern Recognition in NeuroImaging (PRNI). Retrieved from http://hdl.handle.net/11858/00-001M-0000-0014-4F54-C

Tardif, C., Dinse, J., Schäfer, A., Turner, R., & Bazin, P.-L. (2013). Multi-modal surface-based alignment of cortical areas using intra-cortical T1 contrast. In L. Shen, T. Liu, P.-T. Yap, H. Huang, D. Shen, & C.-F. Westin (Eds.), Multimodal brain image analysis (pp. 222–232). Berlin: Springer.

Shiee, N., Bazin, P.-L., Cuzzocreo, J. L., Blitz, A., & Pham, D. L. (2011). Segmentation of brain images using adaptive atlases with application to ventriculomegaly. In Information Processing in Medical Imaging (pp. 1–12). Heidelberg: Springer.

Bogovic, J. A., Bazin, P.-L., & Prince, J. L. (2010). Topology-Preserving STAPLE. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 1–6).