49 citations · 147 across the 12 of their papers we have counts for
5 papers · 1 filter
2D/3D Deep Image Registration by Learning 3D Displacement Fields for Abdominal Organs
Ryuto Miura, Megumi Nakao, Mitsuhiro Nakamura +1
Deformable registration of two-dimensional/three-dimensional (2D/3D) images of abdominal organs is a complicated task because the abdominal organs deform significantly and their co…
Feedback Assisted Adversarial Learning to Improve the Quality of Cone-beam CT Images
Takumi Hase, Megumi Nakao, Mitsuhiro Nakamura +1
Unsupervised image translation using adversarial learning has been attracting attention to improve the image quality of medical images. However, adversarial training based on the g…
IGCN: Image-to-graph Convolutional Network for 2D/3D Deformable Registration
Megumi Nakao, Mitsuhiro Nakamura, Tetsuya Matsuda
Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an imag…
Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image
M. Nakao, F. Tong, M. Nakamura +1
Shape reconstruction of deformable organs from two-dimensional X-ray images is a key technology for image-guided intervention. In this paper, we propose an image-to-graph convoluti…
Three-dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction
Megumi Nakao, Keiho Imanishi, Nobuhiro Ueda +3
The reduction of metal artifacts in computed tomography (CT) images, specifically for strong artifacts generated from multiple metal objects, is a challenging issue in medical imag…