Search Results for author: Yoshito Otake

Found 20 papers, 7 papers with code

Automatic hip osteoarthritis grading with uncertainty estimation from computed tomography using digitally-reconstructed radiographs

no code implementations30 Dec 2023 Masachika Masuda, Mazen Soufi, Yoshito Otake, Keisuke Uemura, Sotaro Kono, Kazuma Takashima, Hidetoshi Hamada, Yi Gu, Masaki Takao, Seiji Okada, Nobuhiko Sugano, Yoshinobu Sato

However, as the classification is subjective, we aimed to develop an automated approach to classify the disease severity based on the two grades using digitally-reconstructed radiographs (DRRs) from CT images.


Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography

1 code implementation21 Jul 2023 Yi Gu, Yoshito Otake, Keisuke Uemura, Mazen Soufi, Masaki Takao, Hugues Talbot, Seiji Okada, Nobuhiko Sugano, Yoshinobu Sato

The proposed method achieved high accuracy in BMD estimation, where Pearson correlation coefficients of 0. 880 and 0. 920 were observed for DXA-measured BMD and QCT-measured BMD estimation tasks, respectively, and the root mean square of the coefficient of variation values were 3. 27 to 3. 79% for four measurements with different poses.

Density Estimation

MSKdeX: Musculoskeletal (MSK) decomposition from an X-ray image for fine-grained estimation of lean muscle mass and muscle volume

no code implementations31 May 2023 Yi Gu, Yoshito Otake, Keisuke Uemura, Masaki Takao, Mazen Soufi, Yuta Hiasa, Hugues Talbot, Seiji Okata, Nobuhiko Sugano, Yoshinobu Sato

We propose a method (named MSKdeX) to estimate fine-grained muscle properties from a plain X-ray image, a low-cost, low-radiation, and highly accessible imaging modality, through musculoskeletal decomposition leveraging fine-grained segmentation in CT. We train a multi-channel quantitative image translation model to decompose an X-ray image into projections of CT of individual muscles to infer the lean muscle mass and muscle volume.

Computed Tomography (CT) Image-to-Image Translation +1

Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping

no code implementations27 Mar 2023 Ryo Toda, Hayato Itoh, Masahiro Oda, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku MORI

This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes.


Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration

1 code implementation16 Nov 2019 Robert Grupp, Mathias Unberath, Cong Gao, Rachel Hegeman, Ryan Murphy, Clayton Alexander, Yoshito Otake, Benjamin McArthur, Mehran Armand, Russell Taylor

By using these annotations as training data for neural networks, state of the art performance in fluoroscopic segmentation and landmark localization was achieved.


Region-based Convolution Neural Network Approach for Accurate Segmentation of Pelvic Radiograph

no code implementations29 Oct 2019 Ata Jodeiri, Reza A. Zoroofi, Yuta Hiasa, Masaki Takao, Nobuhiko Sugano, Yoshinobu Sato, Yoshito Otake

With the increasing usage of radiograph images as a most common medical imaging system for diagnosis, treatment planning, and clinical studies, it is increasingly becoming a vital factor to use machine learning-based systems to provide reliable information for surgical pre-planning.

Data Augmentation Multi-Task Learning +1

Fast and Automatic Periacetabular Osteotomy Fragment Pose Estimation Using Intraoperatively Implanted Fiducials and Single-View Fluoroscopy

no code implementations22 Oct 2019 Robert Grupp, Ryan Murphy, Rachel Hegeman, Clayton Alexander, Mathias Unberath, Yoshito Otake, Benjamin McArthur, Mehran Armand, Russell Taylor

The relative pose of the fragment is established by estimating the movement of the two BB constellations using a single fluoroscopic view taken after osteotomy and fragment relocation.

Pose Estimation

Pelvis Surface Estimation From Partial CT for Computer-Aided Pelvic Osteotomies

no code implementations23 Sep 2019 Robert Grupp, Yoshito Otake, Ryan Murphy, Javad Parvizi, Mehran Armand, Russell Taylor

In order to reduce radiation exposure, we propose a new smooth extrapolation method leveraging a partial pelvis CT and a statistical shape model (SSM) of the full pelvis in order to estimate a patient's complete pelvis.

Anatomy Test

Smooth Extrapolation of Unknown Anatomy via Statistical Shape Models

no code implementations23 Sep 2019 Robert Grupp, Hsin-Hong Chiang, Yoshito Otake, Ryan Murphy, Chad Gordon, Mehran Armand, Russell Taylor

The three extrapolation techniques evaluated were: copying and pasting of the surface estimate (non-smooth baseline), a feathering between the patient surface and surface estimate, and an estimate generated via a Thin Plate Spline trained from displacements between the surface estimate and corresponding vertices of the known patient surface.


Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modeling

1 code implementation21 Jul 2019 Yuta Hiasa, Yoshito Otake, Masaki Takao, Takeshi Ogawa, Nobuhiko Sugano, Yoshinobu Sato

We evaluated validity of the uncertainty metric in the multi-class organ segmentation problem and demonstrated a correlation between the pixels with high uncertainty and the segmentation failure.

Active Learning Organ Segmentation +1

Pose Estimation of Periacetabular Osteotomy Fragments with Intraoperative X-Ray Navigation

2 code implementations22 Mar 2019 Robert B. Grupp, Rachel A. Hegeman, Ryan J. Murphy, Clayton P. Alexander, Yoshito Otake, Benjamin A. McArthur, Mehran Armand, Russell H. Taylor

Results: In simulation, average fragment pose errors were 1. 3{\deg}/1. 7 mm when the planned fragment matched the intraoperative fragment, 2. 2{\deg}/2. 1 mm when the plan was not updated to match the true shape, and 1. 9{\deg}/2. 0 mm when the fragment shape was intraoperatively estimated.

Pose Estimation

Dynamic Block Matching to assess the longitudinal component of the dense motion field of the carotid artery wall in B-mode ultrasound sequences -- Association with coronary artery disease

no code implementations6 Sep 2018 Guillaume Zahnd, Kozue Saito, Kazuyuki Nagatsuka, Yoshito Otake, Yoshinobu Sato

Purpose: The motion of the common carotid artery tissue layers along the vessel axis during the cardiac cycle, observed in ultrasound imaging, is associated with the presence of established cardiovascular risk factors.

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