Výsledky vyhledávání - "Uemura, Keisuke"

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3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation

Autor: Gu, Yi, Otake, Yoshito, Uemura, Keisuke, Takao, Masaki, Soufi, Mazen, Okada, Seiji, Sugano, Nobuhiko, Talbot, Hugues, Sato, Yoshinobu

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 possibility of various clinical applications, but achieving clinically

Externí odkaz: http://arxiv.org/abs/2409.16702

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Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images

Autor: Soufi, Mazen, Otake, Yoshito, Iwasa, Makoto, Uemura, Keisuke, Hakotani, Tomoki, Hashimoto, Masahiro, Yamada, Yoshitake, Yamada, Minoru, Yokoyama, Yoichi, Jinzaki, Masahiro, Kusano, Suzushi, Takao, Masaki, Okada, Seiji, Sugano, Nobuhiko, Sato, Yoshinobu

Deep learning-based image segmentation has allowed for the fully automated, accurate, and rapid analysis of musculoskeletal (MSK) structures from medical images. However, current approaches were either applied only to 2D cross-sectional images, addre

Externí odkaz: http://arxiv.org/abs/2409.02770

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Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray Image

Autor: Gu, Yi, Otake, Yoshito, Uemura, Keisuke, Takao, Masaki, Soufi, Mazen, Okada, Seiji, Sugano, Nobuhiko, Talbot, Hugues, Sato, Yoshinobu

While most vision tasks are essentially visual in nature (for recognition), some important tasks, especially in the medical field, also require quantitative analysis (for quantification) using quantitative images. Unlike in visual analysis, pixel val

Externí odkaz: http://arxiv.org/abs/2407.20495

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

Autor: Masuda, Masachika, Soufi, Mazen, Otake, Yoshito, Uemura, Keisuke, Kono, Sotaro, Takashima, Kazuma, Hamada, Hidetoshi, Gu, Yi, Takao, Masaki, Okada, Seiji, Sugano, Nobuhiko, Sato, Yoshinobu

Progression of hip osteoarthritis (hip OA) leads to pain and disability, likely leading to surgical treatment such as hip arthroplasty at the terminal stage. The severity of hip OA is often classified using the Crowe and Kellgren-Lawrence (KL) classi

Externí odkaz: http://arxiv.org/abs/2401.00159

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Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower Extremities

Autor: Li, Ganping, Otake, Yoshito, Soufi, Mazen, Taniguchi, Masashi, Yagi, Masahide, Ichihashi, Noriaki, Uemura, Keisuke, Takao, Masaki, Sugano, Nobuhiko, Sato, Yoshinobu

Purpose: Manual annotations for training deep learning (DL) models in auto-segmentation are time-intensive. This study introduces a hybrid representation-enhanced sampling strategy that integrates both density and diversity criteria within an uncerta

Externí odkaz: http://arxiv.org/abs/2307.13986

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

Autor: Gu, Yi, Otake, Yoshito, Uemura, Keisuke, Soufi, Mazen, Takao, Masaki, Talbot, Hugues, Okada, Seiji, Sugano, Nobuhiko, Sato, Yoshinobu

Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a decline in daily living activities. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) are highly accurate for diagnosing ost

Externí odkaz: http://arxiv.org/abs/2307.11513

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

Autor: Gu, Yi, Otake, Yoshito, Uemura, Keisuke, Takao, Masaki, Soufi, Mazen, Hiasa, Yuta, Talbot, Hugues, Okata, Seiji, Sugano, Nobuhiko, Sato, Yoshinobu

Musculoskeletal diseases such as sarcopenia and osteoporosis are major obstacles to health during aging. Although dual-energy X-ray absorptiometry (DXA) and computed tomography (CT) can be used to evaluate musculoskeletal conditions, frequent monitor

Externí odkaz: http://arxiv.org/abs/2305.19920

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BMD-GAN: Bone mineral density estimation using x-ray image decomposition into projections of bone-segmented quantitative computed tomography using hierarchical learning

Autor: Gu, Yi, Otake, Yoshito, Uemura, Keisuke, Soufi, Mazen, Takao, Masaki, Sugano, Nobuhiko, Sato, Yoshinobu

We propose a method for estimating the bone mineral density (BMD) from a plain x-ray image. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) provide high accuracy in diagnosing osteoporosis; however, these modalities

Externí odkaz: http://arxiv.org/abs/2207.03210

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