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Found 7 papers, 3 papers with code
$\texttt{NePhi}$: Neural Deformation Fields for Approximately Diffeomorphic Medical Image Registration
In contrast to the predominant voxel-based transformation fields used in learning-based registration approaches, NePhi represents deformations functionally, leading to great flexibility within the design space of memory consumption during training and inference, inference time, registration accuracy, as well as transformation regularity.
GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency
We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration.
$\texttt{GradICON}$: Approximate Diffeomorphisms via Gradient Inverse Consistency
We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration.
LiftReg: Limited Angle 2D/3D Deformable Registration
We propose LiftReg, a 2D/3D deformable registration approach.
Computer Aided Detection for Pulmonary Embolism Challenge (CAD-PE)
Objective: To generate a database of annotated computed tomography pulmonary angiographies, use it to compare the sensitivity and false positive rate of current algorithms and to develop new methods that improve such metrics.
Generative-based Airway and Vessel Morphology Quantification on Chest CT Images
For validation, we first use synthetically generated airways and vessels produced by the proposed generative model to compute the relative error and directly evaluate the accuracy of CNR in comparison with traditional methods.
Pulmonary Artery–Vein Classification in CT Images Using Deep Learning
In this paper, we present a novel, fully automatic approach to classify vessels from chest CT images into arteries and veins.
Ranked #3 on
Pulmonary Artery–Vein Classification
on SunYs
