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Found 18 papers, 0 papers with code
GAPNet: Granularity Attention Network with Anatomy-Prior-Constraint for Carotid Artery Segmentation
Atherosclerosis is a chronic, progressive disease that primarily affects the arterial walls.
Fitting tree model with CNN and geodesics to track vesselsand application to Ultrasound Localization Microscopy data
Segmentation of tubular structures in vascular imaging is a well studied task, although it is rare that we try to infuse knowledge of the tree-like structure of the regions to be detected.
Grouping Boundary Proposals for Fast Interactive Image Segmentation
These boundary proposals are then incorporated into the proposed image segmentation model, such that the target segmentation contours are made up of a set of selected boundary proposals and the corresponding geodesic paths linking them.
Fast Marching Energy CNN
Leveraging geodesic distances and the geometrical information they convey is key for many data-oriented applications in imaging.
Chan-Vese Attention U-Net: An attention mechanism for robust segmentation
When studying the results of a segmentation algorithm using convolutional neural networks, one wonders about the reliability and consistency of the results.
A model is worth tens of thousands of examples
Traditional signal processing methods relying on mathematical data generation models have been cast aside in favour of deep neural networks, which require vast amounts of data.
Deformable Voxel Grids for Shape Comparisons
As for the last two, they require to first optimize DVGs on a collection of shapes, which amounts to a pre-processing step.
Geodesic Models with Convexity Shape Prior
The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios.
An Elastica Geodesic Approach With Convexity Shape Prior
The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios.
Geodesic Paths for Image Segmentation with Implicit Region-based Homogeneity Enhancement
Minimal paths are regarded as a powerful and efficient tool for boundary detection and image segmentation due to its global optimality and the well-established numerical solutions such as fast marching method.
A Generalized Asymmetric Dual-front Model for Active Contours and Image Segmentation
The Voronoi diagram-based dual-front active contour models are known as a powerful and efficient way for addressing the image segmentation and domain partitioning problems.
Trajectory Grouping with Curvature Regularization for Tubular Structure Tracking
Tubular structure tracking is a crucial task in the fields of computer vision and medical image analysis.
A Region-based Randers Geodesic Approach for Image Segmentation
In this paper, we introduce a new variational image segmentation model based on the minimal geodesic path framework and the eikonal PDE, where the region-based appearance term that defines then regional homogeneity features can be taken into account for estimating the associated minimal geodesic paths.
From Active Contours to Minimal Geodesic Paths: New Solutions to Active Contours Problems by Eikonal Equations
In this chapter, we give an overview of part of our previous work based on the minimal path framework and the Eikonal partial differential equation (PDE).
Minimal Paths for Tubular Structure Segmentation with Coherence Penalty and Adaptive Anisotropy
In this paper, we propose a new minimal path model associated with a dynamic Riemannian metric embedded with an appearance feature coherence penalty and an adaptive anisotropy enhancement term.
A New Coherence-Penalized Minimal Path Model with Application to Retinal Vessel Centerline Delineation
In this paper, we propose a new minimal path model for minimally interactive retinal vessel centerline extraction.
Global Minimum for a Finsler Elastica Minimal Path Approach
In this paper, we propose a novel curvature-penalized minimal path model via an orientation-lifted Finsler metric and the Euler elastica curve.
A New Finsler Minimal Path Model With Curvature Penalization for Image Segmentation and Closed Contour Detection
This metric is non-Riemannian and asymmetric, defined on an orientation lifted space, incorporating the curvature penalty in the geodesic energy.
