Publications (Method)

Structural Brain Segmentation

Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45–57. https://doi.org/10.1109/42.906424

Billot, B., Magdamo, C., Cheng, Y., Arnold, S. E., Das, S., & Iglesias, J. E. (2023). Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets. Proceedings of the National Academy of Sciences of the United States of America, 120(9), e2216399120. https://doi.org/10.1073/pnas.2216399120

Puonti, O., Iglesias, J. E., & Van Leemput, K. (2016). Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling. NeuroImage, 143, 235–249. https://doi.org/10.1016/j.neuroimage.2016.09.011 

Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., & Dale, A. M. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341–355. https://doi.org/10.1016/s0896-6273(02)00569-x 

Vesselness Filters

Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998). Multiscale vessel enhancement filtering. In W. M. Wells, A. Colchester, & S. Delp (Eds.), Medical image computing and computer-assisted intervention — MICCAI’98 (Lecture Notes in Computer Science, Vol. 1496, pp. 130–137). Springer. https://doi.org/10.1007/BFb0056195

Merveille, O., Talbot, H., Najman, L., & Passat, N. (2018). Curvilinear structure analysis by ranking the orientation responses of path operators. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(2), 304–317. https://doi.org/10.1109/TPAMI.2017.2672972

Jerman, T., Pernuš, F., Likar, B., & Špiclin, Ž. (2015). Beyond Frangi: An improved multiscale vesselness filter. In Medical Imaging 2015: Image Processing (Vol. 9413, 94132A). SPIE. https://doi.org/10.1117/12.2081147

PVS 3D Modelling

Bernal, J., Valdés-Hernández, M. D. C., Escudero, J., Duarte, R., Ballerini, L., Bastin, M. E., Deary, I. J., Thrippleton, M. J., Touyz, R. M., & Wardlaw, J. M. (2022). Assessment of perivascular space filtering methods using a three-dimensional computational model. Magnetic resonance imaging, 93, 33–51. https://doi.org/10.1016/j.mri.2022.07.016 

Duarte Coello, R., Valdés Hernández, M. D. C., Zwanenburg, J. J. M., van der Velden, M., Kuijf, H. J., De Luca, A., Moyano, J. B., Ballerini, L., Chappell, F. M., Brown, R., Jan Biessels, G., & Wardlaw, J. M. (2024). Detectability and accuracy of computational measurements of in-silico and physical representations of enlarged perivascular spaces from magnetic resonance images. Journal of neuroscience methods, 403, 110039. https://doi.org/10.1016/j.jneumeth.2023.110039 

Valdés Hernández, M. D. C., Duarte Coello, R., Xu, W., Bernal, J., Cheng, Y., Ballerini, L., Wiseman, S. J., Chappell, F. M., Clancy, U., Jaime García, D., Arteaga Reyes, C., Zhang, J. F., Liu, X., Hewins, W., Stringer, M., Doubal, F., Thrippleton, M. J., Jochems, A., Brown, R., & Wardlaw, J. M. (2024). Influence of threshold selection and image sequence in in-vivo segmentation of enlarged perivascular spaces. Journal of neuroscience methods, 403, 110037. https://doi.org/10.1016/j.jneumeth.2023.110037 

Waiter, G., Lambrou, T., Leontidis, G., Oren, N., Morris, T., Gordon, S., McGowan, J., & Nicolson, C. (Eds.). (2023). Frontiers abstract book: 27th Conference on Medical Image Understanding and Analysis 2023. Frontiers Media SA. https://doi.org/10.3389/9782832512319