5 citations · 7 across the 5 of their papers we have counts for
9 papers
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…
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…