Context
Subject
Candidate profile
PhD thesis information
- 36 month funding from ANR (French National Research Agency).
- Location: Creatis laboratory in INSA Lyon.
- Advisors: Dr. Odyssée Merveille, Prof. Carole Frindel, Prof. Nicolas Passat
- Applications to be sent by mail to odyssee.merveille@creatis.insa-lyon.fr with a detailed CV, covering letter, and optionally recommendation letters from former advisors and latest grade transcripts.
References
[1] G. Bertrand et M. Couprie. “Powerful parallel and symmetric 3D thinning schemes based on critical kernels”. Journal of Mathematical Imaging and Vision 48 (2014), p. 134-148.
[2] J. Clough et al. “A topological loss function for deep-learning based image segmentation using persistent homology”. IEEE Transactions on Pattern Analysis and Machine Intelligence (In Press).
[3] M. Haft-Javaherian et al. “A topological encoding convolutional neural network for segmentation of 3D multiphoton images of brain vasculature using persistent homology”. Computer Vision and Pattern Recognition Workshops, Procs. 2020, p. 990-991.
[4] A. Hilbert et al. “BRAVE-NET : Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease”. Frontiers in Artificial Intelligence 3 (2020), p. 78.
[5] O. Merveille et al. “nD Variational Restoration of Curvilinear Structures with Prior-Based Directional Regularization”. IEEE Transactions on Image Processing 28 (2019), p. 3848-3859.
[6] D. Mozaffarian et al. “Heart disease and stroke statistics—2015 update : a report from the American Heart Association”. Circulation 131 (2015), e29-e322.
[7] O. Oktay et al. “Anatomically constrained neural networks (ACNNs) : application to cardiac image enhancement and segmentation”. IEEE Transactions on Medical Imaging 37 (2017), p. 384-395.
[8] N. Passat et al. “Magnetic resonance angiography: From anatomical knowledge modeling to vessel segmentation”. Medical Image Analysis 10 (2006), p. 259-274.
[9] L. Picard et al. Recommendation of the WFITN regarding simulation in neurointerventional training. 2017.
[10] G. Tetteh et al. “Deepvesselnet : Vessel segmentation, centerline prediction, and bifurcation detection in 3-d angiographic volumes”. Frontiers in Neuroscience 14 (2020).
[11] X. Xie et al. “A survey on incorporating domain knowledge into deep learning for medical image analysis”. Medical Image Analysis 69 (2021), p. 101985.