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Found 15 papers, 1 papers with code
Cardiac ultrasound simulation for autonomous ultrasound navigation
However, the image quality varies with operator skills as acquiring and interpreting ultrasound images requires extensive training due to the imaging artefacts, the range of acquisition parameters and the variability of patient anatomies.
A multi-organ point cloud registration algorithm for abdominal CT registration
Registering CT images of the chest is a crucial step for several tasks such as disease progression tracking or surgical planning.
A Wide-area, Low-latency, and Power-efficient 6-DoF Pose Tracking System for Rigid Objects
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.
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix
We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration.
Graph convolutional regression of cardiac depolarization from sparse endocardial maps
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.
Towards Automatic Manipulation of Intra-cardiac Echocardiography Catheter
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.
Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI
We propose to learn a probabilistic motion model from a sequence of images.
Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations
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.
Learning a Probabilistic Model for Diffeomorphic Registration
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
Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation
In this paper, we propose a novel model framework for learning automatic X-ray image parsing from labeled CT scans.
Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration
This model enables to also generate normal or pathological deformations of any new image based on the probabilistic latent space.
Dilated FCN for Multi-Agent 2D/3D Medical Image Registration
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.
An Artificial Agent for Robust Image Registration
The resulting registration approach inherently encodes both a data-driven matching metric and an optimal registration strategy (policy).
A Self-Taught Artificial Agent for Multi-Physics Computational Model Personalization
Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model.
Shaping the Future through Innovations: From Medical Imaging to Precision Medicine
Medical images constitute a source of information essential for disease diagnosis, treatment and follow-up.
