1 code implementation • 30 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.
1 code implementation • 26 Jul 2023 • Ganping Li, Yoshito Otake, Mazen Soufi, Masashi Taniguchi, Masahide Yagi, Noriaki Ichihashi, Keisuke Uemura, Masaki Takao, Nobuhiko Sugano, Yoshinobu Sato
The combination of the proposed method and our BAL framework provides a semi-automatic way for efficient annotation of medical image datasets.
1 code implementation • 21 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.
no code implementations • 31 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.
no code implementations • 27 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.
1 code implementation • 7 Jul 2022 • Yi Gu, Yoshito Otake, Keisuke Uemura, Mazen Soufi, Masaki Takao, Nobuhiko Sugano, Yoshinobu Sato
We propose a method for estimating the bone mineral density (BMD) from a plain x-ray image.
no code implementations • 9 Jan 2022 • Masahiro Oda, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Kensaku MORI
Our method recognizes and segments lung normal and infection regions in CT volumes.
no code implementations • 9 Jan 2022 • Masahiro Oda, Tong Zheng, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku MORI
We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions.
1 code implementation • 21 Dec 2020 • Keisuke Uemura, Yoshito Otake, Masaki Takao, Mazen Soufi, Akihiro Kawasaki, Nobuhiko Sugano, Yoshinobu Sato
A training dataset was created by manually segmenting the regions of the phantom for 40 cases (20 cases each).
1 code implementation • 16 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.
no code implementations • 29 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.
no code implementations • 26 Oct 2019 • Ata Jodeiri, Yoshito Otake, Reza A. Zoroofi, Yuta Hiasa, Masaki Takao, Keisuke Uemura, Nobuhiko Sugano, Yoshinobu Sato
Alignment of the bones in standing position provides useful information in surgical planning.
no code implementations • 22 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.
no code implementations • 23 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.
no code implementations • 23 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.
1 code implementation • 21 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.
no code implementations • 27 Jun 2019 • Mitsuki Sakamoto, Yuta Hiasa, Yoshito Otake, Masaki Takao, Yuki Suzuki, Nobuhiko Sugano, Yoshinobu Sato
Our goal was to develop an automated segmentation method of the bones and muscles in the postoperative CT images.
2 code implementations • 22 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.
no code implementations • 6 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.
no code implementations • 18 Mar 2018 • Yuta Hiasa, Yoshito Otake, Masaki Takao, Takumi Matsuoka, Kazuma Takashima, Jerry L. Prince, Nobuhiko Sugano, Yoshinobu Sato
To evaluate image synthesis, we investigated dependency of image synthesis accuracy on 1) the number of training data and 2) the gradient consistency loss.