no code implementations • 15 Mar 2022 • Samuel Joutard, Thomas Pheiffer, Chloe Audigier, Patrick Wohlfahrt, Reuben Dorent, Sebastien Piat, Tom Vercauteren, Marc Modat, Tommaso Mansi
Registering CT images of the chest is a crucial step for several tasks such as disease progression tracking or surgical planning.
no code implementations • 15 Sep 2021 • Young-Ho Kim, Ankur Kapoor, Tommaso Mansi, Ali Kamen
Overall, our proposed tracking system is capable of tracking a rigid object pose with sub-millimeter accuracy at the mid range of the work space and sub-degree accuracy for all work space under a lab setting.
no code implementations • 3 Nov 2020 • Julian Krebs, Hervé Delingette, Nicholas Ayache, Tommaso Mansi
We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration.
no code implementations • 28 Sep 2020 • Felix Meister, Tiziano Passerini, Chloé Audigier, Èric Lluch, Viorel Mihalef, Hiroshi Ashikaga, Andreas Maier, Henry Halperin, Tommaso Mansi
The training set consists of data produced by a computational model of cardiac electrophysiology on a large cohort of synthetically generated geometries of ischemic hearts.
no code implementations • 12 Sep 2020 • Young-Ho Kim, Jarrod Collins, Zhongyu Li, Ponraj Chinnadurai, Ankur Kapoor, C. Huie Lin, Tommaso Mansi
We present a simplified calibration approach for error compensation and verify with complex rotation of the catheter in benchtop and phantom experiments under varying realistic curvature conditions.
no code implementations • 31 Jul 2019 • Julian Krebs, Tommaso Mansi, Nicholas Ayache, Hervé Delingette
We propose to learn a probabilistic motion model from a sequence of images.
no code implementations • 22 Mar 2019 • Chen Qin, Bibo Shi, Rui Liao, Tommaso Mansi, Daniel Rueckert, Ali Kamen
The proposed registration approach is then built on the factorized latent shape code, with the assumption that the intrinsic shape deformation existing in original image domain is preserved in this latent space.
no code implementations • 18 Dec 2018 • Julian Krebs, Hervé Delingette, Boris Mailhé, Nicholas Ayache, Tommaso Mansi
Besides, we visualized the learned latent space and show that the encoded deformations can be used to transport deformations and to cluster diseases with a classification accuracy of 83% after applying a linear projection.
Ranked #1 on Diffeomorphic Medical Image Registration on Automatic Cardiac Diagnosis Challenge (ACDC) (using extra training data)
Deformable Medical Image Registration Diffeomorphic Medical Image Registration +1
no code implementations • 11 Jun 2018 • Yue Zhang, Shun Miao, Tommaso Mansi, Rui Liao
In this paper, we propose a novel model framework for learning automatic X-ray image parsing from labeled CT scans.
no code implementations • 19 Apr 2018 • Julian Krebs, Tommaso Mansi, Boris Mailhé, Nicholas Ayache, Hervé Delingette
This model enables to also generate normal or pathological deformations of any new image based on the probabilistic latent space.
no code implementations • 22 Nov 2017 • Shun Miao, Sebastien Piat, Peter Fischer, Ahmet Tuysuzoglu, Philip Mewes, Tommaso Mansi, Rui Liao
Second, to handle various artifacts in 2D X-ray images, multiple local agents are employed efficiently via FCN-based structures, and an auto attention mechanism is proposed to favor the proposals from regions with more reliable visual cues.
1 code implementation • 30 Nov 2016 • Rui Liao, Shun Miao, Pierre de Tournemire, Sasa Grbic, Ali Kamen, Tommaso Mansi, Dorin Comaniciu
The resulting registration approach inherently encodes both a data-driven matching metric and an optimal registration strategy (policy).
no code implementations • 1 May 2016 • Dominik Neumann, Tommaso Mansi, Lucian Itu, Bogdan Georgescu, Elham Kayvanpour, Farbod Sedaghat-Hamedani, Ali Amr, Jan Haas, Hugo Katus, Benjamin Meder, Stefan Steidl, Joachim Hornegger, Dorin Comaniciu
Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model.
no code implementations • 1 May 2016 • Dorin Comaniciu, Klaus Engel, Bogdan Georgescu, Tommaso Mansi
Medical images constitute a source of information essential for disease diagnosis, treatment and follow-up.