9 citations · 10 across the 4 of their papers we have counts for
4 papers
3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation
Yi Gu, Yoshito Otake, Keisuke Uemura +6
Radiography is widely used in orthopedics for its affordability and low radiation exposure. 3D reconstruction from a single radiograph, so-called 2D-3D reconstruction, offers the p…
Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images
Mazen Soufi, Yoshito Otake, Makoto Iwasa +12
Deep learning-based image segmentation has allowed for the fully automated, accurate, and rapid analysis of musculoskeletal (MSK) structures from medical images. However, current a…
Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural network
Keisuke Uemura, Yoshito Otake, Masaki Takao +4
Purpose: To apply a convolutional neural network (CNN) to develop a system that segments intensity calibration phantom regions in computed tomography (CT) images, and to test the s…
Estimation of Pelvic Sagittal Inclination from Anteroposterior Radiograph Using Convolutional Neural Networks: Proof-of-Concept Study
Ata Jodeiri, Yoshito Otake, Reza A. Zoroofi +5
Alignment of the bones in standing position provides useful information in surgical planning. In total hip arthroplasty (THA), pelvic sagittal inclination (PSI) angle in the standi…