No abstract
We use a simple yet powerful higher-order conditional random field (CRF) to model optical flow. It consists of a standard photoconsistency cost and a prior on affine motions both modeled in terms of higher-order potential functions. Reasoning jointly over a large set of unknown variables provides more reliable motion estimates and a robust matching criterion. One of the main contributions is that unlike previous region-based methods, we omit the assumption of constant flow. Instead, we consider local affine warps whose likelihood energy can be computed exactly without approximations. This results in a tractable, so-called, higher-order likelihood function. We realize this idea by employing triangulation meshes which immensely reduce the complexity of the problem. Optimization is performed by hierarchical fusion moves and an adaptive mesh refinement strategy. Experiments show that we achieve high-quality motion fields on several data sets including the Middlebury optical flow database.
No abstract
Despite rapid advances in interventional imaging, the navigation of a guide wire through abdominal vasculature remains, not only for novice radiologists, a difficult task. Since this navigation is mostly based on 2D fluoroscopic image sequences from one view, the process is slowed down significantly due to missing depth information and patient motion. We propose a novel approach for 3D dynamic roadmapping in deformable regions by predicting the location of the guide wire tip in a 3D vessel model from the tip's 2D location, respiratory motion analysis, and view geometry. In a first step, the method compensates for the apparent respiratory motion in 2D space before backprojecting the 2D guide wire tip into three dimensional space, using a given projection matrix. To countervail the error connected to the projection parameters and the motion compensation, as well as the ambiguity caused by vessel deformation, we establish a statistical framework, which computes a reliable estimate of the guide wire tip location within the 3D vessel model. With this 2D-to-3D transfer, the navigation can be performed from arbitrary viewing angles, disconnected from the static perspective view of the fluoroscopic sequence. Tests on a realistic breathing phantom and on synthetic data with a known ground truth clearly reveal the superiority of our approach compared to naïve methods for 3D roadmapping. The concepts and information presented in this paper are based on research and are not commercially available.
International audienceWe propose a Markov Random Field formulation for the tracking of needles in fluoroscopic images. A novel motion model makes it possible to capture the primarily rigid motion as well as deformations of the needle in a single second-order MRF graph. Needles are represented by B-splines and each control point is associated with a random variable in a MAP-MRF formulation. In addition to the control points we introduce a single additional random variable representing the rigid transformation needles undergo during interventions. The incorporation of rigid transformations allows to recover transformations even in the presence of large displacements which is not possible with existing MRF models for medical tool tracking
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.