Search Results for author: Yoshinobu Sato

Found 13 papers, 5 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.

Classification

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

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

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

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

no code implementations20 Dec 2018 Juan J. Cerrolaza, Mirella Lopez-Picazo, Ludovic Humbert, Yoshinobu Sato, Daniel Rueckert, Miguel Angel Gonzalez Ballester, Marius George Linguraru

With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.

Anatomy

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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