Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
MI |
2022-01-27 10:09 |
Online |
Online |
Disease progression modeling of hip osteoarthrosis based on musculoskeletal CT segmentation Ganping Li, Mazen Soufi, Yoshito Otake (NAIST), Keisuke Uemura, Masaki Takao, Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) MI2021-71 |
[more] |
MI2021-71 pp.111-116 |
MI |
2022-01-27 10:22 |
Online |
Online |
Patient-specific musculoskeletal simulation using an elastic volume simulator VIPER and medical image-based modeling Junya Ino, Yoshito Otake, Mazen Soufi (NAIST), Keisuke Uemura, Masaki Takao, Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) MI2021-72 |
We aim at developing a patient-specific musculoskeletal simulation approach for predicting musculoskeletal behavior of m... [more] |
MI2021-72 pp.117-120 |
MI |
2022-01-27 10:48 |
Online |
Online |
Test-time Geometric Normalization to Cope With Foot Posture Variation in Musculoskeletal Segmentation from CT Images Ito Naoki, Mazen Soufi, Yoshito Otake (NAIST), Takuma Miyamoto, Yasuhito Tanaka (Nara Med. Univ.), Keisuke Uemura, Masaki Takao, Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) MI2021-73 |
[more] |
MI2021-73 pp.121-126 |
MI |
2021-03-15 15:30 |
Online |
Online |
Evaluation of Bayesian Active Learning for Segmentation of Liver and Spleen in Large Scale Abdominal MR Data Sets Bin Zhang, Yoshito Otake, Mazen Soufi (NAIST), Masatoshi Hori (Kobe University), Noriyuki Tomiyama (Osaka University), Yoshinobu Sato (NAIST) MI2020-60 |
Manual annotation in image segmentation is time-consuming and expensive. In order to obtain large number of annotated da... [more] |
MI2020-60 pp.62-65 |
MI |
2020-09-03 09:45 |
Online |
Online |
Extraction of three-dimensional bone structure from CT images of diseased hip using Bayesian U-net Hiroki Makino, Yoshito Otake, Keisuke Uemura (NAIST), Masaki Takao, Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) MI2020-18 |
[more] |
MI2020-18 pp.7-11 |
MI |
2020-09-03 11:15 |
Online |
Online |
Preliminary Investigation on Automatic Segmentation of Lower Leg Muscles in CT Images using Bayesian U-Net: Towards Lower Extremity Biomechanical Analysis
-- Bayesian U-Net Based Muscle Segmentation -- Albrecht Yordanus Erwin Dodu, Mazen Soufi, Yoshito Otake (NAIST), Takuma Miyamoto, Yasuhito Tanaka (Nara Med. Univ.), Keisuke Uemura (NAIST), Masaki Takao, Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) MI2020-24 |
[more] |
MI2020-24 pp.31-33 |
SSS |
2020-03-17 14:10 |
Tokyo |
(Cancelled but technical report was issued) |
Theories and their practices for safety
-- Looking back on and foreseeing the past and coming half centuries -- Yoshinobu Sato (IFHQI) SSS2019-30 |
Theories and practices to prevent a recurrence of accident have been important in the past half century. Recently risk a... [more] |
SSS2019-30 pp.5-8 |
EMD, R |
2020-02-14 13:35 |
Shizuoka |
|
Trend on standardization of Dependability
-- Outline of IEC TC 56 2016 Sydney international meeting and drafts from Japan -- Hiroyuki Goto (FDK), Yoshinobu Sato (The IHQI, Tokyo Healthcare Foundation), Fumiaki Harada (D-Tech Partners), Yoshiki Kinoshita (Kanagawa University) R2019-54 EMD2019-54 |
IEC/TC 56 (International Electrotechnical Commission/Technical Committee 56) plenary meeting was held in Shanghai, China... [more] |
R2019-54 EMD2019-54 pp.1-6 |
MI |
2020-01-29 10:15 |
Okinawa |
OKINAWAKEN SEINENKAIKAN |
Analysis of disease classification and musculoskeletal anatomy using medical images and radiology reports in a large-scale medical image database Shuhei Honda, Yoshito Otake (NAIST), Masaki Takao (Osaka Univ.), Eiji Aramaki, Shuntaro Yada, Yuta Hiasa (NAIST), Kento Aida, Shinichi Sato (NII), Akihiro Nishie (Kyushu Univ.), Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) MI2019-69 |
Recently, the environment for the analysis of large databases, such as the large-scale medical image database, have been... [more] |
MI2019-69 pp.19-22 |
MI |
2020-01-29 11:15 |
Okinawa |
OKINAWAKEN SEINENKAIKAN |
Automated analysis of 3D dynamics of foot and ankle joints using robust CT segmentation and multi-rigid 2D-3D registration. Shuntaro Mizoe, Yoshito Otake (NAIST), Takuma Miyamoto (Nara Med Univ.), Mazen Soufi, Yuta Hiasa (NAIST), Shinichi Kosugi (Kosugi Clinic of Orthopedic), Yasuhito Tanaka (Nara Med Univ.), Yoshinobu Sato (NAIST) MI2019-73 |
[more] |
MI2019-73 pp.37-42 |
MI |
2020-01-29 11:25 |
Okinawa |
OKINAWAKEN SEINENKAIKAN |
Evaluation of OpenSim Biomechanical Analysis with Muscle Model Derived from Fiber Tractography in High-Resolution Cryo-section Images Nobuaki Hagioka, Mazen Soufi, Yoshito Otake (NAIST), Masaki Takao, Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) MI2019-74 |
[more] |
MI2019-74 pp.43-46 |
MI |
2020-01-29 16:00 |
Okinawa |
OKINAWAKEN SEINENKAIKAN |
[Short Paper]
Decomposition of individual musculoskeletal of lower extremity using CycleGAN
-- Estimation of left-right volume ratio of muscle and quantitative evaluation with single radiograph -- Naoki Nakanishi, Yuta Hiasa, Yoshito Otake (NAIST), Masaki Takao, Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) MI2019-92 |
[more] |
MI2019-92 pp.123-124 |
MI |
2020-01-29 16:10 |
Okinawa |
OKINAWAKEN SEINENKAIKAN |
Landmark detection of pelvis and femur for preoperative planning of total hip replacement using CNN Yuki Nakanishi (Kobe Univ.), Yoshito Otake (NAIST), Masaki Takao, Nobuhiko Sugano (Osaka Univ.), Yuta Hiasa (NAIST), Yoshiyuki Kagiyama (Univ. of Yamanashi), Toshiya Kaihara (Kobe Univ.), Yoshinobu Sato (NAIST) MI2019-93 |
We have developed an automated preoperative planning system for total hip replacement. This system requires quantitative... [more] |
MI2019-93 pp.125-127 |
MI |
2020-01-30 11:00 |
Okinawa |
OKINAWAKEN SEINENKAIKAN |
High precision metal artifacts reduction in X-ray CT images by Deep Image Prior and sinogram normalization Hiroya Satake, Tatsuya Yokota (NIT), Yoshito Otake, Yoshinobu Sato (NAIST), Hidekata Hontani (NIT) MI2019-103 |
In this paper, we propose a method to improve the normalization accuracy of sinogram by using DeepImage Prior to remove ... [more] |
MI2019-103 pp.169-174 |
SSS |
2019-12-17 14:45 |
Tokyo |
|
Practice for certification of safety devices highly developed
-- Introduction of procedure using IEC/TC72 standards -- Hiroyuki Tabuse (JIA), Kou Kawashima (OMC), Yoshinobu Sato (JC) SSS2019-25 |
Harmonization between international standards and JIS’s is essential for standardization under Agreement on Technical Ba... [more] |
SSS2019-25 pp.11-14 |
PRMU, MI, IPSJ-CVIM [detail] |
2019-09-04 15:40 |
Okayama |
|
Preliminary investigation on decomposition of individual muscles and bones of lower extremity from single radiograph using CycleGAN Naoki Nakanishi, Yuta Hiasa, Yoshito Otake (NAIST), Masaki Takao, Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) PRMU2019-16 MI2019-35 |
Extraction of musculoskeletal structures of lower extremity from medical images is useful for quantitatively understandi... [more] |
PRMU2019-16 MI2019-35 pp.25-30 |
MI |
2019-01-22 10:20 |
Okinawa |
|
Musculotendinous modeling by using fiber tractography of high-resolution cryosectioned images Shogo Tokisue, Yoshito Otake, Mazen Soufi, Norio Fukuda (NAIST), Masaki Takao, Nobuhiko Sugano (Osaka Univ.), Beom Sun Chung (Ajou Univ.), Jin Seo Park (Dongguk Univ.), Yoshinobu Sato (NAIST) MI2018-62 |
[more] |
MI2018-62 pp.13-18 |
MI |
2019-01-23 11:25 |
Okinawa |
|
Musculoskeletal Segmentation of Metal Artifact-Reduced Maxillofacial CT Images Yuka Moritani, Fatemeh Abdolali, Mitsuki Sakamoto, Yoshito Otake (NAIST), Yuko Shigeta, Tomoko Ikawa, Akira Mishima, Takumi Ogawa (Tsurumi Univ.), Yoshinobu Sato (NAIST) MI2018-93 |
[more] |
MI2018-93 pp.139-142 |
MI |
2019-01-23 14:00 |
Okinawa |
|
MI2018-104 |
(To be available after the conference date) [more] |
MI2018-104 pp.183-184 |
MI |
2019-01-23 16:05 |
Okinawa |
|
3D Shape Reconstruction of Distal Forearm Bones from Radiography using Convolutional Neural Network Mototaka Kabashima, Yuta Hiasa, Yoshito Otake (NAIST), Ryoya Shiode, Tsuyoshi Murase (Osaka Univ.), Yoshinobu Sato (NAIST) MI2018-114 |
The 3D bone model extracted from the CT image is used for diagnosis or follow-up. However, the necessity of the CT acqui... [more] |
MI2018-114 pp.235-238 |