5 citations · 9 across the 4 of their papers we have counts for
7 papers
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…
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…