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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI | 2024-09-06 10:00 |
Hiroshima | (Hiroshima, Online) (Primary: On-site, Secondary: Online) |
Comparative Study of Sparse-View CT Image Reconstruction Using Conditional Diffusion Models Hinako Isogai, Mitsuhiro Nakamura, Megumi Nakao (Kyoto Univ.)  MI2024-20 |
This study aims to develop a framework for sparse-view CT reconstruction that uses a conditional diffusion model to dire... [more] | MI2024-20 pp.8-11 |
| MI | 2024-09-06 10:15 |
Hiroshima | (Hiroshima, Online) (Primary: On-site, Secondary: Online) |
Shape Registration for Laparoscopic Images Using Offline Learning Mami Kobayashi, Yuki Kidoguchi, Satoshi Ogiso, Hiroto Nishino, Megumi Nakao (Kyoto Univ.) MI2024-21 |
(To be available after the conference date) [more] | MI2024-21 pp.12-15 |
| MI | 2024-03-03 09:41 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Okinawa, Online) (Primary: On-site, Secondary: Online) |
A preliminary study on deep causal discovery model for image classification Ryohei Motoda, Megumi Nakao (Kyoto Univ.) MI2023-33 |
Although saliency map used in image classification can visualize the regions correlated with predicted class, it cannot ... [more] | MI2023-33 pp.11-14 |
| MI | 2024-03-04 09:00 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Okinawa, Online) (Primary: On-site, Secondary: Online) |
Distance-informed adversarial learning for metal artifact reduction Daisuke Shigemori, Megumi Nakao (Kyoto Univ.) MI2023-62 |
In this study, we propose an adversarial learning framework that utilises distance information from metal to reduce CT m... [more] | MI2023-62 pp.95-98 |
| MI | 2024-03-04 09:48 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Okinawa, Online) (Primary: On-site, Secondary: Online) |
A trial for improvement in image quality of cone-beam CT images using conditional diffusion model Naruki Murahashi, Mitsuhiro Nakamura, Megumi Nakao (Kyoto Univ.) MI2023-66 |
In this study, we propose a method to improve the image quality of CBCT images based on a conditional diffusion model th... [more] | MI2023-66 pp.109-112 |
| MI, MICT | 2023-11-14 14:40 |
Fukuoka | (Fukuoka) | Construction of organ shape atlas by MeshVAE using hierarchical latent variables Ryuichi Umehara, Mitsuhiro Nakamura, Megumi Nakao (Kyoto University) MICT2023-33 MI2023-26 |
[more] | MICT2023-33 MI2023-26 pp.33-36 |
| MI | 2023-05-18 13:00 |
Aichi | Nagoya Congress Center (Aichi) | [Invited Talk]
Deep learning based 2D/3D registration for deformable organs Megumi Nakao (Kyoto Univ.) MI2023-1 |
[more] | MI2023-1 p.1 |
| MI | 2023-03-07 16:26 |
Okinawa | OKINAWA SEINENKAIKAN (Okinawa, Online) (Primary: On-site, Secondary: Online) |
Construction of an organ shape and position atlas using 3D Mesh Variational Autoencoder Ryuichi Umehara, Mitsuhiro Nakamura, Megumi Nakao (Kyoto University) MI2022-124 |
[more] | MI2022-124 pp.205-209 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-20 16:00 |
Kumamoto | Kumamoto University Kurokami Campus (Kumamoto, Online) (Primary: On-site, Secondary: Online) |
Application of LambdaNetwork learning long-range interactions between pixels to metal artifact detection Daisuke Shigemori, Megumi Nakao, Tetsuya Matsuda (Kyoto Univ.) SIP2022-27 BioX2022-27 IE2022-27 MI2022-27 |
Although deep learning-based image transformation has been attempted to be applied to metal artifact reduction, feature ... [more] | SIP2022-27 BioX2022-27 IE2022-27 MI2022-27 pp.138-143 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-20 16:20 |
Kumamoto | Kumamoto University Kurokami Campus (Kumamoto, Online) (Primary: On-site, Secondary: Online) |
Visualization of Important Features for Classifier Decisions using Deep Image Synthesis Yushi Haku, Megumi Nakao, Tetsuya Matsuda (Kyoto Univ.) SIP2022-28 BioX2022-28 IE2022-28 MI2022-28 |
It is difficult to know the basis for the decisions of machine learning models, and it is necessary to provide a highly ... [more] | SIP2022-28 BioX2022-28 IE2022-28 MI2022-28 pp.144-149 |
| MI | 2022-01-26 13:39 |
Online | Online (Online) | Deep Learning based 2D/3D deformable Image Registration for Abdominal Organs Ryuto Miura, Megumi Nakao, Mitsuhiro Nakamura, Tetsuya Matsuda (Kyoto Univ.) MI2021-62 |
2D/3D image registration is a problem that solves the deformation and alignment of a pre-treatment 3D image to a 2D proj... [more] | MI2021-62 pp.70-75 |
| MI, MICT [detail] | 2021-11-05 09:00 |
Online | Online (Online) | Deformable model registration for a single projection image by learning displacement fields Megumi Nakao, Mitsuhiro Nakamura, Tetsuya Matsuda (Kyoto Univ.) MICT2021-27 MI2021-25 |
(To be available after the conference date) [more] | MICT2021-27 MI2021-25 pp.1-6 |
| MI, MICT [detail] | 2021-11-05 09:20 |
Online | Online (Online) | Force estimation in forceps manipulation of ex-vivo organs from a single-viewpoint camera image Hikaru Toda, Megumi Nakao, Kimihiko Masui, Naoto Kume, Tetsuya Matsuda (Kyoto Univ.) MICT2021-28 MI2021-26 |
In laparoscopic surgery including robotic surgery, it is not possible to accurately measure the contact force applied to... [more] | MICT2021-28 MI2021-26 pp.7-12 |
| MI | 2021-03-15 13:45 |
Online | Online (Online) | Surgical planning model generation by extracting important feature sets in mandibular reconstruction Kazuki Nagai, Megumi Nakao (Kyoto Univ.), Nobuhiro Ueda (Nara Medical Univ.), Yuichiro Imai (Rakuwakai Otowa Hospital), Toshihide Hatanaka, Tadaaki Kirita (Nara Medical Univ.), Tetsuya Matsuda (Kyoto Univ.) MI2020-54 |
Because implicit medical knowledge and experience are used to perform medical treatment, such decisions must be clarifie... [more] | MI2020-54 pp.29-34 |
| MI | 2021-03-15 14:00 |
Online | Online (Online) | Analysis of important features in surgical planning for mandibular reconstruction among multiple surgeons Yusuke Hatakeyama, Kazuki Nagai, Megumi Nakao, Tetsuya Matsuda (Kyoto Univ.) MI2020-55 |
Surgeons perform surgical treatment by considering the facilities and policies of medical institutions and their own exp... [more] | MI2020-55 pp.35-40 |
| MI | 2021-03-16 14:00 |
Online | Online (Online) | Deformable mesh registration of partial lung shapes based on learning of pneumothorax deformation Hinako Maekawa, Megumi Nakao (Kyoto Univ.), Katsutaka Mineura (Kyoto Univ. Hospital), Toyofumi F. Chen-Yoshikawa (Nagoya Univ. Hospital), Tetsuya Matsuda (Kyoto Univ.) MI2020-74 |
Intraoperative pneumothorax is accompanied by large deformation including rotation. As intraoperative cone-beam CT (CBCT... [more] | MI2020-74 pp.112-117 |
| MI | 2020-09-03 10:00 |
Online | Online (Online) | Lung region segmentation of thoracoscopic image with unsupervised image translation Jumpei Nitta, Megumi Nakao (Kyoto Univ.), Keiho Imanishi (e-Growth Co. Ltd.), Tetsuya Matsuda (Kyoto Univ.) MI2020-19 |
In endoscopic surgery, it is necessary to understand the three-dimensional structure of the target region to improve saf... [more] | MI2020-19 pp.13-18 |
| MI | 2020-09-03 14:10 |
Online | Online (Online) | Reconstruction of 3D Organ Shape from a Single X-ray Image using Graph Convolutional Network Fei Tong, Megumi Nakao (Kyoto Univ.), Shuqiong Wu (Osaka Univ.), Mitsuhiro Nakamura, Tetsuya Matsuda (Kyoto Univ.) MI2020-28 |
[more] | MI2020-28 pp.45-50 |
| MI | 2020-09-03 14:25 |
Online | Online (Online) | Proposal of 3D Generative Adversarial Network for Improving Image Ouality of Cone-Beam CT Images Takumi Hase, Megumi Nakao (Kyoto Univ.), Keoho Imanishi (e-Growth Co., Ltd), Mitsuhiro Nakamura, Tetsuya Matsuda (Kyoto Univ.) MI2020-29 |
Artifacts and defects included in Cone-beam CT (CBCT) images have become an obstacle in radiation therapy and surgery su... [more] | MI2020-29 pp.51-56 |
| MI | 2020-09-03 14:40 |
Online | Online (Online) | Analysis of pneumothorax deformation for in vivo animal lungs using model-based registration Kotaro Kobayashi, Megumi Nakao, Junko Tokuno (Kyoto Univ.), Toyofumi F Chen-Yoshikawa (Nagoya Univ.), Tetsuya Matsuda (Kyoto Univ.) MI2020-30 |
(To be available after the conference date) [more] | MI2020-30 pp.57-62 |
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