activity
20162022
most citedLearning a Probabilistic Model for Diffeomorphic Registration

250 citations · 534 across the 17 of their papers we have counts for

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10 papers · 1 filter

cs.CV2022★ 2 cited

A multi-organ point cloud registration algorithm for abdominal CT registration

Samuel Joutard, Thomas Pheiffer, Chloe Audigier +6

Registering CT images of the chest is a crucial step for several tasks such as disease progression tracking or surgical planning. It is also a challenging step because of the heter…

cs.CV2020★ 1 cited

Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix

Julian Krebs, Hervé Delingette, Nicholas Ayache +1

We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the…

cs.CV2019★ 21 cited

Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI

Julian Krebs, Tommaso Mansi, Nicholas Ayache +1

We propose to learn a probabilistic motion model from a sequence of images. Besides spatio-temporal registration, our method offers to predict motion from a limited number of frame…

cs.CV2019★ 1 cited

Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations

Chen Qin, Bibo Shi, Rui Liao +3

We propose a fully unsupervised multi-modal deformable image registration method (UMDIR), which does not require any ground truth deformation fields or any aligned multi-modal imag…

cs.CV2018★ 250 cited

Learning a Probabilistic Model for Diffeomorphic Registration

Julian Krebs, Hervé Delingette, Boris Mailhé +2

We propose to learn a low-dimensional probabilistic deformation model from data which can be used for registration and the analysis of deformations. The latent variable model maps…

cs.CV2018★ 223 cited

Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation

Yue Zhang, Shun Miao, Tommaso Mansi +1

Automatic parsing of anatomical objects in X-ray images is critical to many clinical applications in particular towards image-guided invention and workflow automation. Existing dee…

cs.CV2018

Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration

Julian Krebs, Tommaso Mansi, Boris Mailhé +2

We propose a deformable registration algorithm based on unsupervised learning of a low-dimensional probabilistic parameterization of deformations. We model registration in a probab…

cs.CV2017★ 15 cited

Dilated FCN for Multi-Agent 2D/3D Medical Image Registration

Shun Miao, Sebastien Piat, Peter Fischer +4

2D/3D image registration to align a 3D volume and 2D X-ray images is a challenging problem due to its ill-posed nature and various artifacts presented in 2D X-ray images. In this p…

cs.CV2016★ 13 cited

An Artificial Agent for Robust Image Registration

Rui Liao, Shun Miao, Pierre de Tournemire +4

3-D image registration, which involves aligning two or more images, is a critical step in a variety of medical applications from diagnosis to therapy. Image registration is commonl…

cs.CV2016

Shaping the Future through Innovations: From Medical Imaging to Precision Medicine

Dorin Comaniciu, Klaus Engel, Bogdan Georgescu +1

Medical images constitute a source of information essential for disease diagnosis, treatment and follow-up. In addition, due to its patient-specific nature, imaging information rep…