The 5th Conference on Medical and Biological Imaging
Program & Abstract
March 10, 2018
Sigma Hall, Osaka University

生体医用画像研究会 第5回若手発表会
プログラム・抄録集

2018年3月10日
大阪大学 基礎工学国際棟

- CONTENTSJSMBE-MBI2017-01

Automated segmentation of renal arterial branches from abdominal contrast enhanced
CT images using Convolutional Neural Network and knowledge of vessel anatomy: A
comparison of network structures
Yuki Suzuki (NAIST, Osaka University), Mariko Ono, Yuta Hiasa (NAIST),
Masatoshi Hori (Osaka University), Yoshito Otake (NAIST), Noriyuki Tomiyama (Osaka University),
Yoshinobu Sato(NAIST)
JSMBE-MBI2017-02

Synthetic Biosignal Generation Based on Generative Adversarial Networks
Shota Harada, Hideaki Hayashi, Seiichi Uchida (Kyushu University)
JSMBE-MBI2017-03[Invited Lecture] The role of machine learning/architectural intelligence incomputer-aided diagnosis systems
Mitsutaka Nemoto (Kindai University)
JSMBE-MBI2017-04

Kinematic analysis of forearm rotation including subluxation by 2D-3D registration
using biplane fluoroscopy.
Yusuke Tenma, Yuta Hiasa, Yoshito Otake (NAIST), Shingo Abe, Tsuyoshi Murase (Osaka University),
Yoshinobu Sato (NAIST)
JSMBE-MBI2017-05

Improvement of athlete performance and injury prevention research using IoT
Hiromi Takahata, Issei Ogasawara, Takuya Magome, Akira Uchiyama, Shouji Konda,
Teruki Yokoyama, Yuri Kitamura, Ken Nakata (Osaka University)
JSMBE-MBI2017-06

Reconstruction of 3-Dimensional Rotator Cuff Tear Shape using Magnetic Resonance Images
Kazuki Ishiro, Kento Morita, Manabu Nii (University of Hyogo), Tomoyuki Muto, Hiroshi Tanaka,
Hiroaki Inui (Nobuhara Hospital), Syoji Kobashi (University of Hyogo), Katsuya Nobuhara(Nobuhara Hospital)
JSMBE-MBI2017-07

Estimation of muscle fiber direction from 3D ultrasound image with mechanical linear
guide system and reconstruction of 4D muscle motion
Norio Fukuda, Tokumi Sano, Yoshito Otake (NAIST), Shingo Abe, Shoji Konda (Osaka University),
Yoshinobu Sato (NAIST)
JSMBE-MBI2017-08

A New Image-based Method for Vortex Structure Analysis of the Developing Human
Fetal HeartRyo Haraguchi (University of Hyogo), Saori Nishitani, Hirohiko Imai,
Tetsuya Takakuwa (Kyoto University)

̶目次̶
JSMBE-MBI2017-01

畳み込みニューラルネットワークと解剖学的知識を用いた腹部造影 CT 画像からの腎動脈枝の自動抽出−ネットワーク構造の比較−
鈴木 裕紀（奈良先端科学技術大学院大学，大阪大学），小野 真理子，日朝 祐太（奈良先端科学技術大学院大学），堀 雅敏（大阪大学），大竹 義人（奈良先端科学技術大学院大学），
富山 憲幸（大阪大学），佐藤 嘉伸（奈良先端科学技術大学院大学）
JSMBE-MBI2017-02

Generative Adversarial Networks に基づく人工生体信号生成原田 翔太, Hideaki Hayashi, Seiichi Uchida（九州大学）
JSMBE-MBI2017-03[招待講演] 画像診断支援システムにおける機械学習・人工知能の役割
根本 充貴（近畿大学）
JSMBE-MBI2017-04

2 方向 X 線透視動画像を用いた 2D-3D レジストレーションによる亜脱臼動作を含む前腕回旋動態解析
天満 勇介，日朝 祐太，大竹 義人（奈良先端科学技術大学院大学），阿部 真悟，
村瀬 剛（大阪大学），佐藤 嘉伸（奈良先端科学技術大学院大学）
JSMBE-MBI2017-05

IoT を活用したアスリートパフォーマンス向上と障害予防研究
高畑 裕美，小笠原 一生，馬込 卓弥，内山 彰，近田 彰司，横山 光樹，橋詰 謙，
喜多村 祐里，中田 研（大阪大学）
JSMBE-MBI2017-06

MR 画像を用いた肩腱板断裂 3 次元形状の再構築
井城 一輝，盛田 健人，新居 学（兵庫県立大学），無藤 智之，田中 洋，乾 浩明（信原病院），
小橋 昌司（兵庫県立大学），信原 克哉（信原病院）
JSMBE-MBI2017-07

機械式リニアガイドによる 3 次元超音波画像を用いた筋繊維方向推定および筋肉の 4 次元動態復元
福田 紀生，佐野 徳美，大竹 義人（奈良先端科学技術大学院大学），阿部 慎吾，近田彰治（大阪大学），佐藤 嘉伸（奈良先端科学技術大学院大学）
JSMBE-MBI2017-08

ヒト胎児心臓心尖部における渦構造の画像解析手法の提案
原口 亮（兵庫県立大学），西谷 早織，今井 宏彦，高桑 徹也（京都大学）

日本生体医工学会 専門別研究会 生体医用画像研究会
若手発表会 2018/3
Proceedings of Medical and Biological Imaging - JSMBE 2018/3

JSMBE-MBI2017-01 (2018-03)

Automated segmentation of renal arterial branches from abdominal contrast enhanced CT images using Convolutional Neural Network and knowledge of vessel anatomy: A comparison of
network structures
Yuki Suzuki*,**, Mariko Ono*, Yuta Hiasa*, Masatoshi Hori***, Yoshito Otake*, Noriyuki Tomiyama***, Yoshinobu
Sato** Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan.
** Graduate School of Information Science and Technology, Osaka University, Osaka, Japan.
*** Graduate School of Medicine, Osaka University, Osaka, Japan.

Abstract
To make a surgical plan for surgeries such as organ transplantation or resection, it is necessary for medical doctors tounderstand vascular structure and its positional relationship between organs. To understand vascular structure in abdominal
region, contrast enhanced CT images are widely used, but complex structure of abdominal vessels makes it hard and laborious. For this reason, it is valuable to automatically extract the vascular region from 3D-CT images with a computer tosupport doctors.
Line enhancement filtering has been widely used for the segmentation of tubular structure. While line enhancement filtering works effectively for tubular structure with certain radius and curvature, it tends to fail to enhance thin vessel withsmall radius. Convolutional Neural Network (CNN) has been making huge success in computer vision, and medical andbiological imaging. In this paper, we propose automated segmentation of renal arterial branches using CNN.
In the experiment using 3D-CT images and manually labelled renal arterial regions, existing method using line filter andproposed method using CNN were compared.
Keywords: segmentation, convolutional neural network

This article has been printed without reviewing and editing as received from the authors, and the copyright of the article
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4日本生体医工学会 専門別研究会 生体医用画像研究会
若手発表会 2018/3
Proceedings of Medical and Biological Imaging - JSMBE 2018/3

JSMBE-MBI2017-02 (2018-03)

Synthetic Biosignal Generation Based on Generative Adversarial
Networks
Shota Harada*, Hideaki Hayashi**, Seiichi Uchida*** Graduate School of Systems Life Sciences, Kyushu University, Fukuoka, Japan.
** Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan.

Abstract
Biosignals strongly reflect human internal states. In particular, the human body including diseases can cause visible
changes in the patterns of biosignals. Disease can therefore be detected by classifying patterns of biosignals. Many studies
on classification of biosignals using machine learning techniques including deep learning have been reported. Thanks to
deep learning, some studies achieved drastic increase in classification accuracy. One problem with deep learning is that alarge amount of training data is required to obtain enough accuracy. However, the number of training data is often limitedin the real-world biosignal classification problems.
In this study, we propose a time-series data generation method based on Generative Adversarial Networks (GANs) toincrease the number of training data of biosignals. GANs are deep neural network frameworks contained of two neuralnetworks. In the proposed method, each neural network is developed based on a long short-term memories (LSTM) for its
hidden layers. Thereby allowing the adaptation of GANs framework to time-series data generation.
In the experiments, we confirmed the capability of the proposed method for generating biosignals. To show the effectiveness of the proposed method, the results using the proposed method and the existing three data augmentation methods werecompared.
Keywords: Neural networks in biosignal classification, Biosignal data augmentation.

This article has been printed without reviewing and editing as received from the authors, and the copyright of the article
belongs to the authors.
5日本生体医工学会 専門別研究会 生体医用画像研究会
若手発表会 2018/3
Proceedings of Medical and Biological Imaging - JSMBE 2018/3

JSMBE-MBI2017-03 (2018-03)

The Role of Machine Learning / Architectural Intelligence in Computer-Aided Diagnosis Systems
Mitsutaka Nemoto** Faculty of Biology-Oriented Science and Technology (B.O.S.T.), Kindai University, Wakayama, Japan.

Abstract
Recently, architectural intelligence (AI) is paid the most attention. We introduce the properties of AI (e.g., what AI
can/cannot do). We also introduce some application examples of AI for medical image analysis and computer-aided diagnosis/detection.
Keywords: Machine learning, Architectural Intelligence (AI), Computer-Aided Diagnosis (CAD/CADx), ComputerAided Detection (CADe).

This article has been printed without reviewing and editing as received from the authors, and the copyright of the article
belongs to the authors.
6日本生体医工学会 専門別研究会 生体医用画像研究会
若手発表会 2018/3
Proceedings of Medical and Biological Imaging - JSMBE 2018/3

JSMBE-MBI2017-04 (2018-03)

Kinematic analysis of forearm rotation including subluxation by
2D-3D registration using biplane fluoroscopy
Yusuke Tenma*, Yuta Hiasa*, Yoshito Otake*, Shingo Abe**, Tsuyoshi Murase**, Yoshinobu Sato*,* Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan** Department of Orthopaedic Biomaterial Science, Graduate School of Medicine, Osaka University, Osaka, Japan.

Abstract
In orthopedic surgery, the quantitative kinematic analysis of skeletal structures is important to improve accuracy of thepatient-specific treatment. In case of the forearm, 3D motion analysis of pronation and supination is important for diagnosis
and treatment planning. Previous studies analyzed the 3D rotation axis during the movement between maximum pronationand maximum supination using CTs at three postures [1]. In order to understand the movement of the forearm in more detailin a clinical routine, a method which allows 3D motion analysis with high temporal resolution while keeping the radiationdose at the level comparable to the conventional x-ray imaging is preferable. Our specific goal in this study is to analyze
the detailed mechanism of instability of the elbow and wrist joints including a quick movement. For this purpose, we propose
a 2D-3D registration method using a calibrated biplanar dynamic fluoroscopy and a CT image. The method follows that of
Otake et al [2] which optimizes the rigid transformation parameters of multiple bones by maximizing the similarity between
the fluoroscopy and digitally reconstructed radiograph (DRR).
In this paper, we report three evaluation experiments which used x-ray images of (1) a phantom
model of the bones with soft tissue, (2) a healthy
volunteer, and (3) a patient acquired in a clinicalprotocol. Through these experiments, we evaluatedthe accuracy of the proposed methods and analyzed the kinematics of forearm rotation including
a quick movement. Fig.1 shows the 3D kinematics
estimated from the clinical fluoroscopy images.
Fig. 2 shows the translation of the radius with respect to the coordinate system of the humerus
bone. The proposed method was able to obtain the
kinematics of the forearm in the case of subluxation owing to the high temporal resolution of the xray fluoroscopy.

(a)

(b)

(c)

(d)

Fig.1 Example visualization of the analyzed 3D kinematics using the clinicalfluoroscopy images. (a) lateral image (LAT), (b) anteroposterior image (AP),(c) and (d) LAT and AP images overlaid with the estimated 3D bone position.

Fig.2 The translation of radius with respect to the coordinate system of the humerus.

Keywords: kinematic analysis, forearm, 2D-3D registration，x-ray fluoroscopy

References[1] Kiyoko Kazama, Koichi Kobayashi, et al. "In vivo three-dimensional analysis of distal radioulnar joint kinematics
during forearm pronation-supination” Journal of Biomechanical Science and Engineering 11 (2016) 15-00364[2] Otake, Yoshito, et al. "3D–2D registration in mobile radiographs: algorithm development and preliminary clinicalevaluation." Physics in medicine and biology 60.5 (2015): 207

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7

日本生体医工学会 専門別研究会 生体医用画像研究会
若手発表会 2018/3
JSMBE-MBI2017-05 (2018-03)

Proceedings of Medical and Biological Imaging - JSMBE 2018/3

Improvement of athlete performance and injury prevention research using IoT (IoT を活用したアスリートパフォーマンス向上と障害予防研究)
Hiromi Takahata*, Issei Ogasawara*, Takuya Magome*, Akira Uchiyama**, Shouji Konda*, Teruki Yokoyama*, Yuri
Kitamura*, Ken Nakata** Department of Graduate School of Medicine Faculty of Medicine, Osaka University, Osaka, Japan.
** Department of Graduate School of Information Science and Technology, Osaka University, Osaka, Japan.

Abstract
Promote the use of IoT to realize support for strengthening players for the purpose of improving Japanese sports competitiveness. Then, Cooperation is achieved with Japan sports promotion center (JSC), a national sports science center (JISS),the university where cooperation cooperates and a private enterprise. And different field fusion related to a sport (medicine,nutrition, information science, engineering and physical education) is promoted. We will establish "sports research alljapan research base" to conduct comprehensive sports related research. In this method, performance index of athlete isderived by utilizing plural sensing devices. Also, by measuring the changes in body weight in detail, we will also detect
the sign of injury.
That programs a production course of the sports medical department science researcher who contributes to the world anddoes leader education of human resources of the sports medical department science field.
Keywords: Sports Medical Science, Sports IoT, Injury prevention

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belongs to the authors.
8日本生体医工学会 専門別研究会 生体医用画像研究会
若手発表会 2018/3
JSMBE-MBI2017-06 (2018-03)

Proceedings of Medical and Biological Imaging - JSMBE 2018/3

Reconstruction of 3-Dimensional Rotator Cuff Tear Shape using
Magnetic Resonance Images
Kazuki Ishiro*, Kento Morita*, Manabu Nii*, Tomoyuki Muto**, Hiroshi Tanaka**, Hiroaki Inui**, Syoji Kobashi*,
Katsuya Nobuhara*** Graduate School of Engineering, University of Hyogo, Hyogo, Japan.
** Nobuhara Hospital and Institute of Biomechanics, Hyogo, Japan.

Abstract
The shoulder rotator cuff is a group of muscle tendons that includes supraspinatus, infraspinatus, subscapularis and teresminor. Tears of the rotator cuff are a common musculoskeletal injury in which the tendons are pulled off from their insertion
on the humerus bone. This is known as rotator cuff tear (RCT). In serious case, the patients have to undergo surgical operation because it often causes acute pain and limits the movement of the shoulder. According to location, direction and sizeof RCT, the orthopedic surgeon select the operative method. Therefore, the diagnosis of RCT shape is very important for
its treatment. Currently, magnetic resonance (MR) images are used to diagnose RCT shapes. However, it is not easy todetermine RCT shape because MR images have a spacing between slices. In this study, we propose a method to reconstruction of 3-Dimensional RCT shape from MR images. It is aimed to support the diagnosis. Firstly, we have extracted three
regions, humeral head, normal cuff and torn cuff, from MR images. Secondly, we have calculated implicit function basedon these regions. Then, the function values of slice gap have been interpolated by the radial based function. Finally, we haveobtained 3-D RCT shape by rendering the function. The proposed method has been applied in RCT patients’ MR images.
We have obtained smooth and natural 3-D RCT shapes through our proposed method. And then, we have diagnosed RCT
type and size by using the 3-D shapes. In the result, the accuracy diagnosed by 3-D shapes is higher than using only MR
images. The result shows that the proposed method successfully assists orthopedic surgeons in their treatment of RCT.
Keywords: rotator cuff tear, magnetic resonance imaging, 3-dimensional shape, implicit function.

This article has been printed without reviewing and editing as received from the authors, and the copyright of the article
belongs to the authors.
9日本生体医工学会 専門別研究会 生体医用画像研究会
若手発表会 2018/3
Proceedings of Medical and Biological Imaging - JSMBE 2018/3

JSMBE-MBI2017-07 (2018-03)

Estimation of muscle fiber direction from 3D ultrasound image
with mechanical linear guide system and reconstruction of 4D
muscle motion
Norio Fukuda*, Tokumi Sano*, Yoshito Otake*, Shingo Abe**, Shoji Konda**, Yoshinobu Sato** Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan.
** Department of Orthopaedic Medical Engineering, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.

Abstract
Analysis of three-dimensional structure of muscles and its movement is important for sports science and rehabilitation.
A dynamic-2D ultrasound image is widely used for analyzing muscle function because the ultrasound images have hightemporal resolution and high contrast of muscle fascicles [1]. However, there is no information of outside the scanningplane so the three-dimensional structure cannot be acquired. On the other hand, a common 3D ultrasound image has low
temporal resolution and limited field-of-view (FOV). Therefore our goal is to reconstruct a dynamic 3D ultrasound image
which has high temporal resolution and large FOV by combining the dynamic 2D ultrasound image and static large 3D
ultrasound image. In this study, we developed a mechanical guide system for reconstructing large 3D ultrasound volume
with high accuracy. We create dynamic-3D volume by integration of static-3D and dynamic-2D for analyzing muscle function with the mechanical guide system. We used the slice-to-volume non-rigid registration [2] to identify deformation nearthe 2D plane and reconstruct dynamic-3D data by interpolating the 2D planes. In the experiments, we compared the reconstruction accuracy of the freehand system and the proposed linear guide system using a block phantom and we validated
the accuracy of slice-to-volume non-rigid registration by simulation and real image.
Keywords: Ultrasound，Muscle function analysis，Muscle fiber analysis.

Figure 1. Overview of the reconstruction method of a dynamic 3D ultrasound image with a linear guide scanningsystem.
References[1] Monetti, Giuseppe, Marco Fogli, and Gianluigi Mele. "Ultrasound of Muscle and Tendon Pathology." Muscle and Tendon Injuries.
Springer, Berlin, Heidelberg, 2017. 75-86.
[2] Ferrante, Enzo, and Nikos Paragios. "Non-rigid 2d-3d medical image registration using markov random fields." International Conference
on Medical Image Computing and Computer-Assisted Intervention. Springer, Berlin, Heidelberg, 2013This article has been printed without reviewing and editing as received from the authors, and the copyright of the article
belongs to the authors.
10日本生体医工学会 専門別研究会 生体医用画像研究会
若手発表会 2018/3
Proceedings of Medical and Biological Imaging - JSMBE 2018/3

JSMBE-MBI2017-08 (2018-03)

A New Image-based Method for Vortex Structure Analysis of theDeveloping Human Fetal HeartRyo Haraguchi *, Saori Nishitani **, Hirohiko Imai ***, Tetsuya Takakuwa *** Graduate School of Applied Informatics, University of Hyogo, Kobe, Japan.
** Graduate School of Medicine, Kyoto University, Kyoto, Japan.
*** Graduate School of Informatics, Kyoto University, Kyoto, Japan

Abstract
We have developed a new image-based method for vortex structure analysis of the human fetal heart. It is well known
that human heart has a helical structure of its ventricular wall; however, we currently lack an understanding of how thedetailed vortex structure of its heart apex. In this preliminary study, we have attempted a quantitative analysis of the myocardial architecture of ex vivo fetal hearts by DT-MRI. We have developed the method of finding the vortex center based on
the distribution of the primary eigenvector in the heart apex.
Keywords:fetal heart, vortex, myocardial fiber orientation, DT-MRI

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