49 citations · 149 across the 10 of their papers we have counts for
10 papers
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…
Two-dimensional Ultrasound Imaging Technique based on Neural Network using Acoustic Simulation
Yoshiki Nagatani, Shigeaki Okumura, Shuqiong Wu +1
The two-dimensional (2D) ultrasound imaging is widely used in several fields. In this study, the potential of two-dimensional ultrasound imaging technique based on machine learning…