49 citations · 147 across the 12 of their papers we have counts for
12 papers
Shape Reconstruction from Thoracoscopic Images using Self-supervised Virtual Learning
Tomoki Oya, Megumi Nakao, Tetsuya Matsuda
Intraoperative shape reconstruction of organs from endoscopic camera images is a complex yet indispensable technique for image-guided surgery. To address the uncertainty in reconst…
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…
Kernel-based framework to estimate deformations of pneumothorax lung using relative position of anatomical landmarks
Utako Yamamoto, Megumi Nakao, Masayuki Ohzeki +3
In video-assisted thoracoscopic surgeries, successful procedures of nodule resection are highly dependent on the precise estimation of lung deformation between the inflated lung in…
Statistical modeling of pneumothorax deformation by mapping CT and cone-beam CT images
Megumi Nakao, Hinako Maekawa, Katsutaka Mineura +3
In this study, we introduce statistical modeling methods for pneumothorax deformation using paired cone-beam computed tomography (CT) images. We designed a deformable mesh registra…
Analysis of Heterogeneity of Pneumothorax-associated Deformation using Model-based Registration
Megumi Nakao, Kotaro Kobayashi, Junko Tokuno +3
Recent advances in imaging techniques have enabled us to visualize lung tumors or nodules in early-stage cancer. However, the positions of nodules can change because of intraoperat…
Statistical Deformation Reconstruction Using Multi-organ Shape Features for Pancreatic Cancer Localization
Megumi Nakao, Mitsuhiro Nakamura, Takashi Mizowaki +1
Respiratory motion and the associated deformations of abdominal organs and tumors are essential information in clinical applications. However, inter- and intra-patient multi-organ…
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…
Sparse Elasticity Reconstruction and Clustering using Local Displacement Fields
Megumi Nakao, Mitsuki Morita, Tetsuya Matsuda
This paper introduces an elasticity reconstruction method based on local displacement observations of elastic bodies. Sparse reconstruction theory is applied to formulate the under…
Deformation estimation of an elastic object by partial observation using a neural network
Utako Yamamoto, Megumi Nakao, Masayuki Ohzeki +1
Deformation estimation of elastic object assuming an internal organ is important for the computer navigation of surgery. The aim of this study is to estimate the deformation of an…