The 2nd Conference on Medical and Biological Imaging
Program & Abstract
March 14, 2015
Icho Kaikan, Osaka University	

生体医用画像研究会 第2回若手発表会
プログラム・抄録集
2015年3月14日
大阪大学 銀杏会館

- CONTENTSJSMBE-MBI2014-01

Hierarchical Simulation of Respiration Using Alveolus and Airway Models
Akihiro Wada (Osaka University), Masataka Imura (Osaka University), Shunsuke Yoshimoto (Osaka
University), Osamu Oshiro (Osaka University)
JSMBE-MBI2014-02

A rendering method based on local maximum intensity projection for in vivo cellularimaging
Ryo Taguchi (Osaka University), Shigeto Seno (Osaka University), Junichi Kikuta (Osaka University),
Yoichi Takenaka (Osaka University), Masaru Ishii (Osaka University), Hideo Matsuda (Osaka
University)
JSMBE-MBI2014-03

Estimation of External Forces Using Local Displacement of Elastic Body
Ryohei Sakata (Kyoto University), Megumi Nakao (Kyoto University), Tetsuya Matsuda (Kyoto
University)
JSMBE-MBI2014-04

Three-dimensional Measurement of Fiber Direction in Thigh Muscles: Toward
Computational Anatomy Approach to Musculoskeletal Analysis
Yoshito Otake (Nara Institute of Science and Technology), Futoshi Yokota (Nara Institute of Scienceand Technology), Masaki Takao (Osaka University), Takeshi Ogawa (Osaka University), Norio
Fukuda (Osaka University/Nara Institute of Science and Technology), Keisuke Uemura (Osaka
University), Nobuhiko Sugano (Osaka University), Yoshinobu Sato (Nara Institute of Science andTechnology)
JSMBE-MBI2014-05[Invited Lecture] Cardiovascular studies by computational heart models
Ryo Haraguchi (National Cerebral and Cardiovascular Center)
JSMBE-MBI2014-06

Graph-based unsupervised and semi-supervised learning for prostate extraction in MRI
imagesWeiwei Du(Kyoto Institute of Technology), Shiyang Wang(The University of Chicago), Yahui
Peng(Beijing Jiaotong University), Aytekin Oto(The University of Chicago)
JSMBE-MBI2014-07

A Bone Marrow Recognition Method for Bone Tissue Images using Wavelet Transform
Hironori Shigeta (Osaka University), Tomohiro Mashita (Osaka University), Takeshi Kaneko (Osaka
University), Junichi Kikuta (Osaka University), Shigeto Seno (Osaka University), Haruo Takemura(Osaka University), Hideo Matsuda (Osaka University), Masaru Ishii (Osaka University)
JSMBE-MBI2014-08

A Study on Semi-automatic Fibular Transfer Planning in Mandibular Reconstruction
Shimpei Aso (Kyoto University), Megumi Nakao (Kyoto University), Keiho Imanishi (e-Growth Co.,
Ltd.), Yuichiro Imai (Rakuwakai Otowa Hospital), Nobuhiro Ueda (Nara Medical University),Toshihide Hatanaka (Nara Medical University), Tadaaki Kirita (Nara Medical University), Tetsuya
Matsuda (Kyoto University)
JSMBE-MBI2014-09

Resection Process Modeling Based on 3D Images
Megumi Nakao (Kyoto University), Kojiro Taura (Kyoto University), Tetsuya Matsuda (Kyoto
University)

̶目次̶
JSMBE-MBI2014-01

肺胞と気管支モデルを用いた階層的呼吸シミュレーション
和田 章宏 (大阪大学), 井村 誠孝 (大阪大学), 吉元 俊輔 (大阪大学), 大城 理 (大阪大学)
JSMBE-MBI2014-02

蛍光ライブイメージングデータのレンダリングのための極大値投影法
田口 遼（大阪大学），瀬尾 茂人（大阪大学），菊田 順一（大阪大学），竹中 要一（大阪大学），
石井 優（大阪大学） ，松田 秀雄（大阪大学）
JSMBE-MBI2014-03

弾性体の局所的な変位観測に基づく外力の推定
坂田 良平 (京都大学), 中尾 恵 (京都大学), 松田 哲也 (京都大学)
JSMBE-MBI2014-04

股関節・大腿部の筋肉を対象とした筋線維方向の三次元計測：筋骨格の多元計
算解剖学に向けて
大竹 義人（奈良先端科学技術大学院大学）, 横田 太（奈良先端科学技術大学院大学）, 高尾
正樹（大阪大学）, 小川 剛（大阪大学）, 福田 紀夫（大阪大学／奈良先端科学技術大学院大学）, 上村 圭亮（大阪大学）, 菅野 伸彦（大阪大学）, 佐藤 嘉伸（奈良先端科学技術大学院
大学）
JSMBE-MBI2014-05[特別講演] Computational Heart Model を用いた心臓研究の紹介
原口亮（国立循環器病研究センター）

JSMBE-MBI2014-06

Graph-based unsupervised and semi-supervised learning for prostate extraction in MRI
imagesWeiwei Du (Kyoto Institute of Technology), Shiyang Wang (The University of Chicago), Yahui Peng
(Beijing Jiaotong University), Aytekin Oto (The University of Chicago)
JSMBE-MBI2014-07

ウェーブレット変換を用いた骨組織画像における骨髄腔の認識手法
繁田 浩功（大阪大学）, 間下 以大（大阪大学）, 金子 雄（大阪大学）, 菊田 順一（大阪大学）, 瀬尾 茂人（大阪大学）, 竹村 治雄（大阪大学）, 松田 秀雄（大阪大学）, 石井 優（大阪大学）
JSMBE-MBI2014-08

下顎骨再建術におけるセミオートマティック腓骨移植計画に関する研究
麻生 晋併(京都大学), 中尾 恵(京都大学), 今西 勁峰(イーグロース株式会社), 今井 裕一郎(洛和会音羽病院), 上田 順宏(奈良県立医科大学), 畠中 利英(奈良県立医科大学), 桐田 忠昭(奈良県立医科大学), 松田 哲也(京都大学)
JSMBE-MBI2014-09

三次元画像に基づく臓器切離プロセスのモデリング中尾 恵（京都大学）, 田浦 康二朗（京都大学）, 松田 哲也（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 2015/3
Proceedings of Medical and Biological Imaging - JSMBE 2015/3

JSMBE-MBI2014-01 (2015-03)

Hierarchical Simulation of Respiration
Using Alveolus and Airway Models
Akihiro Wada*, Masataka Imura*, Shunsuke Yoshimoto*, Osamu Oshiro** Department of Mechanical Science and Bioengineering, Graduate School of Engineering Science, Osaka University,
Osaka, Japan.

Abstract
Respiration is accomplished by expansion and contraction of lung and is essential to live. Therefore the mechanismof respiration has been studied in order to treat respiratory disease. To analyze the respiratory mechanism, the relationshipbetween lung deformation and air flow in the lung must be clarified. This study aimed to reveal the respiratory mechanism considering the lung deformation and the air flow in the airway. Respiration was simulated hierarchically by the alveolus and airway models, as shown in Fig. In order to analyze the alveolus shape deformation and the air flow in the alveolus, the authors designed the alveolus model that consists of 3 models of the elastic model, the fluid model, and thecoupled model. Firstly, the elastic model using Kelvin-Voigt model was applied to represent the lung deformation. Secondly, the fluid model using Navier-Stokes equation was applied to calculate the air flow velocity and pressure in the alveolus. Finally, in order to represent the mutual relations in the alveolus, the authors proposed the coupled model using
the immersed boundary method. Further it is necessary to integrate the result of the alveolus simulations to calculate therespiratory volume occurred by the lung deformation. Therefore, the authors proposed the airway model for the integration of result of the alveolus simulations. In the airway model, the flow volume and the pressure loss in the airway were
calculated on the assumption that the airway was comprised of the pipes and the junctions and that the air flow in the airway was regarded as Hagen-Poiseuille flow. In the evaluation experiments, the authors examined the distribution of pressure and flow velocity in the alveolus using the alveolus model. As the result of simulation, the distribution of pressureand flow velocity in alveolus changed according to lung deformation. Moreover the authors estimated respiratory volumeof the simplified lung by the integration of result of the alveolus simulations, and confirmed that the respiratory volume
was changed according to the distribution of the lung tissue elasticity.
Keywords: simulation, lung, deformation, airway, alveolus, immersed boundary.

Lung
External force

Distribution ofpressure and flow volume
in airway

Alveolus model

Airway model

Mutual relations oflung tissue and air flow Integration of
air flow in airway

Alveolus

Distribution ofpressure and flow velocity
in alveolus

Fig. Overview of proposed hierarchical simulation of respiration

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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 2015/3
JSMBE-MBI2014-02 (2015-03)

Proceedings of Medical and Biological Imaging - JSMBE 2015/3

A rendering method based on local maximum intensity
projection for in vivo cellular imaging
Ryo Taguchi*, Shigeto Seno**, Junichi Kikuta***, Yoichi Takenaka**, Masaru Ishii***,****, Hideo Matsuda**,*****

Department of Information and Computer Sciences, School of Engineering Science, Osaka University, Osaka,Japan**
Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan*** Department of Immunology and Cell Biology, Graduate School of Medicine and Frontier Biosciences, Osaka
University, Osaka, Japan**** WPI-Immunology Frontier Research Center, Osaka University, Osaka, Japan

Abstract
In recent years, the development of fluorescence microscopy have enabled us to observe the inside of biological tissues
as 3D image data, and live imaging has become an essential technique to clarify biological mechanisms. Analyzingchanges of cells in biological tissues is getting more important in biology and medicine, and how to analyze slice images
acquired by live imaging is important problem.
Projection that makes 3D data to 2D image, especially Maximum Intensity Projection (MIP) is widely used ininterpreting PET or MRI studies. MIP is a projection method that projects maximum intensity in the way to the plane ofprojection. MIP has the advantages of computationally low cost and unique result of projection without any parametersettings. In addition, for in vivo fluorescence cellular imaging data, MIP is also used in order to enhance contrast andreduce noise by projecting image stack into a single plane (fig.1). However, in the case that some cells overlap on
projection direction, they are showed as one big cell and indistinguishable.
In this study, we proposed to use Local Maximum Intensity Projection (LMIP [1]) for in vivo cellular imaging data, and
we also proposed a modified LMIP (fig.2) to improve the precision of distinguishability between overlapping cells on the
projected plane. As a result, we confirmed that improvement of distinguishability was achieved, by applying some
automatic segmentation methods to the images projected by MIP and our method.
.
Keywords: live imaging, fluorescence microscope, MIP (maximum intensity projection), LMIP

Fig.1 projected image by MIP

Fig.2 projected image by LMIP

[1] Sato, Y., et al. "Local Maximum Intensity Projection (LMIP): A New Rendering Method for Vascular
Visualization." Journal of computer assisted tomography 22.6 (1998): 912-917.

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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 2015/3
JSMBE-MBI2014-03 (2015-03)

Proceedings of Medical and Biological Imaging - JSMBE 2015/3

Estimation of External Forces Using Local Displacement of
Elastic Body
Ryohei Sakata*, Megumi Nakao**, Tetsuya Matsuda*** Department of Electrical and Electronic Engineering, Kyoto University, Kyoto, Japan.
** Graduate School of Informatics, Kyoto University, Kyoto, Japan.

Abstract
Endoscopic and robotic surgery has enabled minimally invasive procedures and allowed surgeons to perform delicate
operation. However, because careful tool operation is required during surgery and current robotic surgery system cannot
transfer forces, surgeons operate organs while estimating contact forces from only visual information. If intraoperative
measurement and physical estimation of the deformed organs are possible during surgery, the state of organs will be recognized physically and quantitatively, which will make the recent surgery more safe and reliable. Although contact forceson the forceps has been investigated, the physical state of organs is not studied well.
This study proposes a new method for estimating external forces based on local displacement observation of elastic
bodies. We assume that the external forces are sparse in manipulating organs that partially connected to other tissues. Local displacement of organs can be measured or estimated from multiple endoscopic cameras or from ultrasound systems.
By applying L1-norm minimization to finite element method, the proposed method computes external forces applied tothe mesh model from local displacements of the partially observed vertices. Some simulation studies are conducted toconfirm the effectiveness of the method. The experiments showed that the external forces are successfully estimated bypartial observation of the deformed shape.
Keywords: Force estimation, L1-norm minimization, Intraoperative support.

Figure 1. Force estimation result and error between the estimated force and the target force. Yellow arrows are the estimation target. The estimated results (green arrows) are computed from displacement of the observed vertices (light blue).

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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 2015/3
Proceedings of Medical and Biological Imaging - JSMBE 2015/3

JSMBE-MBI2014-04 (2015-03)

Three-dimensional Measurement of Muscle Fiber Direction in
Hip and Thigh Muscles: Toward Multidisciplinary Computational
Anatomy Approach to Musculoskeletal Analysis
Yoshito Otake*, Futoshi Yokota*, Masaki Takao**, Takeshi Ogawa**, Norio Fukuda*,**, Keisuke Uemura**, Nobuhiko
Sugano**, 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
This paper outlines our efforts on the modeling of muscle fiber direction. Our approach is twofold: 1) estimation of thefiber direction of the hip and thigh muscles by texture analysis of a series of high-resolution cryosection photographs(Visible Korean Dataset), and 2) its experimental validation via 3D tracing of the fiber direction in cadaver specimens.
The preliminary results demonstrated a reasonable agreement between the model and the cadaveric evaluation. This fibermodeling in combination with a CT-derived patient-specific model of the muscle’s outer structure would offer a new avenue for the patient-specific muscle dynamics analysis toward multidisciplinary computational anatomy.
Keywords: muscle fiber modeling, cadaveric evaluation, multidisciplinary computational anatomy.

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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 2015/3
JSMBE-MBI2014-06 (2015-03)

Proceedings of Medical and Biological Imaging - JSMBE 2015/3

Graph-based unsupervised and semi-supervised learning forprostate extraction in MRI images
Weiwei Du*, Shiyang Wang**, Yahui Peng***, Aytekin Oto*** Department of Information Science, Kyoto Institute of Technology, Kyoto, Japan.
** Department of Radiology, University of Chicago, Chicago, Illinois, U. S.
*** School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.

Abstract
The purpose of this study is to present an algorithm of graph –based unsupervised and semi-supervised learning for extracting prostate from MRI images. And then we only adjust the grayscales of the prostate in order to let radiologists moreeasily detect the cancer regions. The unsupervised scheme can detect entire inhomogeneous regions which are showed in(b). This detectability of inhomogeneous regions is useful for guiding the placement of stokes in a semi-supervised learning. Placements with (b) are stoked by users and showed in (c). The prostate with (d) is extracted by the semi-supervised
learning. Only the prostate is enhanced with equalize histogram method. (e) is the result of the prostate enhancement.
While the whole MRI image with (f) is enhanced with equalize histogram method.
Keywords: Graph-based unsupervised learning, Graph-based semi-supervised learning, Prostate extraction, MRI image

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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 2015/3
Proceedings of Medical and Biological Imaging - JSMBE 2015/3

JSMBE-MBI2014-07 (2015-03)

A Bone Marrow Recognition Method for Bone Tissue Images
using Wavelet Transform
Hironori Shigeta*, Tomohiro Mashita*,**, Takeshi Kaneko***, Junichi Kikuta***, Shigeto Seno*, Haruo Takemura*,**,
Hideo Matsuda*,****, Masaru Ishii***
*
**
***
****

Graduate School of Information Science and Technology, Osaka University
Cybermedia Center, Osaka University
Graduate School of Medicine, Osaka University
Immunology Frontier Research Center, Osaka University

Abstract
We introduce a method for the analysis of a sequence of bone tissue images taken by a two-photon microscope. Because of the difficulty in inspecting the inside of in-vivo bone tissue covered with hard calcium, the behavior of cells inside bone tissue has not been fully understood. On the other hand, application of two-photon microscopy to bone tissue
has enabled in-vivo imaging more locally and deeply, which visualizes how blood flows inside the bone. These images
contain many clues to unveil mechanism of osteoclasts, which absorb bone tissue. It is also expected to cure disease suchas osteoporosis.
In previous work, we have proposed methods to segment the images based on spatial or temporal continuity. Thesemethods require a time-series of images, optimization of parameters, or manual seed input by a user. In this work, we
propose a method to recognize bone marrow regions inside the images by extracting feature values of each pixel and classifying them using Support Vector Machine. Features are mainly retrieved based on wavelet transform, which recognizes
the texture pattern of the images. We prepared 4 time-series of images taken by the microscope and compared them withground truth images. We confirm that our method properly extracts bone marrow regions with optimized wavelet parameters, and better understood important wavelets and parameters for our method.
Keywords: wavelet transform, two-photon excitation microscopy image

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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 2015/3
JSMBE-MBI2014-08 (2015-03)

Proceedings of Medical and Biological Imaging - JSMBE 2015/3

A Study on Semi-automatic Fibular Transfer Planningin Mandibular Reconstruction
Shimpei Aso*, Megumi Nakao*, Keiho Imanishi**, Yuichiro Imai***, Nobuhiro Ueda****,
Toshihide Hatanaka****, Tadaaki Kirita****, Tetsuya Matsuda** Graduate School of Informatics, Kyoto University, Kyoto, Japan.
** e-Growth Co., Ltd., Kyoto, Japan.
*** Department of Oral and Maxillofacial Surgery, Rakuwakai Otowa Hospital, Kyoto, Japan.
**** Department of Oral and Maxillofacial Surgery, Nara Medical University, Nara, Japan

Abstract
In preoperative planning of mandibular reconstruction with fibular segments, it is necessary to determine the osteotomylines and the proper placement of the segments. Interactive planning software [1, 2] is recently utilized in preoperativedecision making, and surgeons are able to objectively analyze the surgical plan based on quantitative indicators. However,a time-consuming trial-and-error process is needed to obtain a better reconstruction pattern because manual 3D operationis required for adjustment of the fibular segments.
This study aims to develop a semi-automatic fibular transfer planning system based on both interactive operation andautomatic placement of fibular segments. We formulate the placement process as an optimization problem that minimizes
the shape error [2] between the surfaces of the patient's original mandible and of the fibular segments. The position of theconnecting points of each segment is explored under the local shape constraint using a gradient-based search algorithm(Figure 1). In this presentation, we report some automatic placement results in 2-segment and 3-segment case and discuss
applicability to practical preoperative planning.
Keywords: Surgical process modeling, automated planning, mandibular reconstruction

Figure 1. Automatic placement results with a gradient-based search algorithm
References[1] A. K. Antony, W. F. Chen, A. Kolokythas, K. A. Weimer, M. N. Cohen, "Use of Virtual Surgery and Stereolithography-Guided Osteotomy for Mandibular Reconstruction with the Free Fibula", Plast. Reconstr. Surg., Vol. 128, No. 5, pp.
1080-1084, 2011.
[2] M. Nakao, M. Hosokawa, Y. Imai, N. Ueda, T. Hatanaka, T. Kirita and T. Matsuda, "Volumetric Fibular Transfer
Planning with Shape-Based Indicators in Mandibular Reconstruction", IEEE Journal of Biomedical and Health Informatics, Vol. 19, No. 2, 2015. DOI: 10.1109/JBHI.2014.2320720

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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 2015/3
Proceedings of Medical and Biological Imaging - JSMBE 2015/3

JSMBE-MBI2014-09 (2015-03)

Resection Process Modeling Based on 3D Images
Megumi Nakao*, Kojiro Taura**, Tetsuya Matsuda** Graduate School of Informatics, Kyoto University, Kyoto, Japan.
** Dept. of Hepato-Biliary-Pancreatic and Transplant Surgery, Kyoto University Hospital, Kyoto, Japan.

Abstract
Virtual planning software using CT/MR images allows preoperative planning of a patient-specific resection path byconsidering three-dimensional (3D) vascular structures as guiding anatomical reference points. A time-series simulation of
the resection process is also desirable to provide a preview of the locally visible anatomical structures in the intraoperative
deformed state. In the conventional modeling of incisions, tetrahedral elements of the organ model are divided or replaced,
and their surfaces are rendered to visualize deformation [1]. However, the computation time is prohibitively expensive
due to the large number of vertices needed to adequately represent the physical behavior.
In this study, we introduce resection process modeling methods to create cutting simulations with deformations basedon 3D CT/MR images (Figure 1). A sparse tetrahedral mesh is first constructed to enclose the organ region. For a given
cutting point, the vertices are relocated to satisfy the geometrical constraints of the resection path. The mesh deformationis computed using the finite element method and rendered volumetrically by slicing the tetrahedral mesh [2, 3]. This approach models smooth resection paths and produces a high-quality visual simulation of the resection process. Real-timeanimation at greater than 10 frames/s is possible because vertex addition is not required. Moreover, the only manual stepin the setup process is the segmentation of the target organ. In this presentation we demonstrate some simulation results inliver resection.
Keywords: Surgical process modeling, cutting simulation, volume deformation and liver resection

Figure 1. Visualizing liver resection process with volume deformation
Acknowledgements
This research was supported by a Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Scientific
Research for Young Scientists (A), Number 24680059.
References[1] Wu, Jun and Westermann, Rüdiger and Dick, Christian, "Physically-based Simulation of Cuts in Deformable Bodies:
A Survey", Eurographics - State of the Art Reports, 2014.
[2] M. Nakao and K. Minato, "Physics-based Interactive Volume Manipulation for Sharing Surgical Process", IEEE
Trans. on Information Technology in Biomedicine, Vol.14, No. 3, pp. 809-816, 2010.
[3] M. Nakao, Y. Oda, K. Taura, and K. Minato, "Direct Volume Manipulation for Visualizing Intraoperative Liver Resection Process", Computer Methods and Programs in Biomedicine, Vol. 113, No. 3, pp. 725-735, 2014.

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