FaustoMilletari

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Fausto Milletarì

Me

Profile

Fausto Milletari is part of the NVIDIA healthcare team where he works as Senior Solutions Architect. After earning his M.Sc. in informatics, passed with high distinction, he obtained his Ph.D. at the chair for Computer Aided Medical Procedures, directed by Professor Nassir Navab. Fausto's major research topic is segmentation of medical images. He has pioneered the usage of 3D convolutional neural networks to segment MRI and Ultrasound volumetric data and other 3D deep-learning techniques to impose shape priors and regularization on the results. In addition, he works on a variety of other computer vision problems, such as object tracking and detection. His work focuses on pattern recognition and machine learning, and in particular on deep learning techniques. Several of his contributions have been presented in recent editions of MICCAI, IPCAI, BMVC, 3DV and in specialized journals. His current job at NVIDIA focuses on the application of artificial intelligence techniques in medicine at the boundary between research and commercial applications.

Contact

Fausto Milletarì, MSc.

Room: 03.13.037
Email:
Phone: +49 (89) 289-19417

Publications [NOT UPDATED, more at http://bit.ly/2DtwrPn ]

2018
F. Milletari, N. Rieke, M. Baust, M. Esposito, N. Navab
CFCM: Segmentation via Coarse to Fine Context Memory
Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Granada, Spain, September 2018 (bib)
M. Bui, F. Bourier, C. Baur, F. Milletari, N. Navab, S. Demirci
Robust Navigation Support in Lowest Dose Image Setting
International Journal of Computer Assisted Radiology and Surgery, In Press (bib)
2017
M. Riva, C. Hennersperger, F. Milletari, A. Katouzian, F. Pessina, B. Gutierrez-Becker, A. Castellano, N. Navab, L. Bello
3D intra-operative ultrasound and MR image-guidance: pursuing an ultrasound-based management of brainshift to enhance neuronavigation
International Journal of Computer Assisted Radiology and Surgery, in press. (bib)
2016
W. Kehl, F. Milletari, F. Tombari, S. Ilic, N. Navab
Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation
European Conference On Computer Vision (ECCV), Amsterdam, The Netherlands, October 2016 (bib)
G. Bortsova, M. Sterr, L. Wang, F. Milletari, N. Navab, A. Boettcher, H. Lickert, F. Theis, T. Peng
Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields
7th International Workshop on Machine Learning in Medical Imaging (MLMI), Athena, Greece, October 2016 (bib)
C. Kroll, F. Milletari, N. Navab, A. Ahmadi
Coupling Convolutional Neural Networks and Hough Voting for Robust Segmentation of Ultrasound Volumes
Accepted at GCPR 2016 (bib)
F. Milletari, N. Navab, A. Ahmadi
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
International Conference on 3DVision (3DV), Stanford University, California, USA, October 2016, arXiv:1606.04797 (bib)
F. Milletari, A. Ahmadi, C. Kroll, A. Plate, , , , , , K. Bötzel, N. Navab
Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound
arXiv:1601.07014 preprint (bib)
2015
O. Bernard, F. Milletari, A. Ahmadi, et al.
Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography
Institute of Electrical and Electronics Engineers IEEE Transactions on Medical Imaging (bib)
F. Milletari, A. Ahmadi, C. Kroll, C. Hennersperger, F. Tombari, A. Shah, A. Plate, K. Bötzel, N. Navab
Robust Segmentation of Various Anatomies in 3D Ultrasound Using Hough Forests and Learned Data Representations
MICCAI 2015, the 18th International Conference on Medical Image Computing and Computer Assisted Intervention (bib)
F. Milletari, W. Kehl, F. Tombari, S. Ilic, A. Ahmadi, N. Navab
Universal Hough dictionaries for object tracking
British Machine Vision Conference (BMVC), Swansea, UK, September 2015 (bib)
O. Zettinig, A. Shah, C. Hennersperger, M. Eiber, C. Kroll, H. Kübler, T. Maurer, F. Milletari, J. Rackerseder, C. Schulte-zu-Berge, E. Storz, B. Frisch, N. Navab
Multimodal Image-Guided Prostate Fusion Biopsy based on Automatic Deformable Registration
International Journal of Computer Assisted Radiology and Surgery / 6th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI), Barcelona, June 2015.
The original publication is available online at link.springer.com
(bib)
C. Baur, F. Milletari, V. Belagiannis, N. Navab, P. Fallavollita
Automatic 3D reconstruction of electrophysiology catheters from two-view monoplane C-arm image sequences
The 6th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI) (bib)
A. Ahmadi, F. Milletari, N. Navab, M. Schuberth, A. Plate, K. Bötzel
3D Transcranial Ultrasound as a Novel Intra-operative Imaging Technique for DBS surgery - A Feasibility Study
In Proc. 6th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI), Barcelona (SP), June 24, 2015 (bib)
2014
F. Milletari, V. Belagiannis, N. Navab, P. Fallavollita
Fully automatic catheter localization in C-arm images using l1- Sparse Coding
Proceedings of the 17th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Boston, September 2014 (bib)
F. Milletari, M. Yigitsoy, N. Navab, A. Ahmadi
Left Ventricle Segmentation in Cardiac Ultrasound Using Hough-Forests With Implicit Shape and Appearance Priors
MICCAI Challenge on Endocardial Three-dimensional Ultrasound Segmentation (CETUS), Boston, MA, September 2014 (bib)
2013
F. Milletari, N. Navab, P. Fallavollita
Automatic detection of multiple and overlapping EP catheters in fluoroscopic sequences
Proceedings of the 16th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Nagoya, Japan, September 2013 (bib)

Initiatives

Computer vision and medical image analysis Meetup: Link. Participate to the next Meetup RSVP.


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