The 3rd Conference on Medical and Biological Imaging
Program & Abstract
March 12, 2016
Sigma Hall, Osaka University

生体医用画像研究会 第3回若手発表会
プログラム・抄録集
2016年3月12日
大阪大学 基礎工学国際棟

- CONTENTSJSMBE-MBI2015-01

Tomographic Micro-Diagnosis using Multi-Functional OCT: Tissue Classification
Imaging of Atherosclerotic Plaque using ARV- OCT
Daisuke Furukawa (Osaka City University), Souichi Saeki (Osaka City University), Takafumi Hiro
(Nihon University), Masunori Matsuzaki (Yamaguchi University)
JSMBE-MBI2015-02

Segmentation of Surgical Instruments from RGB-D Endoscopic Images using
Convolutional Neural Networks: Preliminary Experiments towards Quantitative Skill
Assessment
Yuta Hiasa (NAIST), Yuki Suzuki (NAIST), Austin Reiter (Johns Hopkins University), Yoshito Otake(NAIST), Masayasu Nishi (Kobe University) , Hitoshi Harada (Kobe University) , Shingo Kanaji(Kobe University), Yoshinobu Sato (NAIST)

JSMBE-MBI2015-03[Invited Lecture] Biomechanical Structure and Haptic interaction
Yoshihiro Kuroda (Osaka University)

JSMBE-MBI2015-04 (Symposium "New Trends in Medical and Biological Imaging Part II")

[Symposium] Application of hyperspectral imaging in medicine
Takashi Nagaoka (Kindai University)
JSMBE-MBI2015-05 (Symposium "New Trends in Medical and Biological Imaging Part II")

[Symposium] Medical and Biological Imaging for Bioscience
Shigeto Seno (Osaka University)
JSMBE-MBI2015-06 (Symposium "New Trends in Medical and Biological Imaging Part II")

[Symposium] Medical Imaging in Sport Science: From Segmental-motion Analysis
toward Anatomical Joint-motion Analysis in Biomechanics
Shoji Konda (Osaka University)
JSMBE-MBI2015-07 (Symposium "New Trends in Medical and Biological Imaging Part II")

[Symposium] Interactive techniques for medical volume segmentation and theirapplication to flower modelingTakashi Ijiri (Ritsumeikan University)
JSMBE-MBI2015-08 (Symposium "New Trends in Medical and Biological Imaging Part II")

[Symposium] Biomedical Images for Simulation and Analysis of Surgical Procedures
Megumi Nakao, Tetsuya Matsuda (Kyoto University)

JSMBE-MBI2015-09

A Study on Sparse Shape Modeling for Fibular Transfer Planning in Mandibular
Reconstruction
Riho Kawasaki (Kyoto University), Megimi Nakao (Kyoto University), Yuichiro Imai (Rakuwakai
Otowa Hospital), Nobuhiro Ueda (Nara Medical University), Toshihide Hatanaka (Nara Medical
University), Mao Shiba (Nara Medical University), Tadaaki Kirita (Nara Medical University), Tetsuya
Matsuda (Kyoto University)
JSMBE-MBI2015-10

Multi-scaled bioimaging with near-infrared luminescence and cathodoluminescence
Shoichiro Fukushima (Osaka University), Taichi Furukawa (Institute for Nanoscience Design, Osaka
University), Hirohiko Niioka (Osaka University), Masayoshi Ichimiya (Osaka University, The
University of Shiga Prefecture), Takumi Sannomiya (Tokyo Institute of Technology), Masaaki Ashida(Osaka University), Jun Miyake (Osaka University), Tsutomu Araki (Osaka University), Mamoru
Hashimoto (Osaka University)
JSMBE-MBI2015-11

A Proposal on Biomechanically-combined Deformable Image Registration for Cervical
Cancer Radiotherapy
Nadhifa Ayunisa (Osaka University), Yoshihiro Kuroda (Osaka University), Noriyuki Kadoya (Tohoku
University), Shunsuke Yoshimoto (Osaka University), Osamu Oshiro (Osaka University)
JSMBE-MBI2015-12

Computational Cardiac Modeling Project using Human Specimens
Ryo Haraguchi, Wataru Ueki, Yoshiaki Morita, Taka-aki Matsuyama, Hatsue Ishibashi-Ueda, Haruka
Nakada (NCVC), Hirofumi Seo (SCIEMENT)̶目次̶
JSMBE-MBI2015-01

多機能 OCT によるマイクロ断層可視化診断
ARV-OCT を用いた動脈硬化プラークの組織性状イメージング
古川 大介（大阪市立大学），佐伯 壮一（大阪市立大学），廣 高史（日本大学），松崎 益徳（山口大学）JSMBE-MBI2015-02

Segmentation of Surgical Instruments from RGB-D Endoscopic Images using
Convolutional Neural Networks: Preliminary Experiments towards Quantitative Skill
Assessment
Yuta Hiasa (NAIST), Yuki Suzuki (NAIST), Austin Reiter (Johns Hopkins University), Yoshito Otake(NAIST) , Masayasu Nishi (Kobe University) , Hitoshi Harada (Kobe University), Shingo Kanaji(Kobe University), Yoshinobu Sato (NAIST)

JSMBE-MBI2015-03[特別講演] 生体構造と力触覚インタラクション
黒田 嘉宏（大阪大学）

JSMBE-MBI2015-04

シンポジウム「生体医用画像の新潮流 II」

Application of hyperspectral imaging in medicine
永岡 隆 （近畿大学）
JSMBE-MBI2015-05

シンポジウム「生体医用画像の新潮流 II」

Medical and Biological Imaging for Bioscience
瀬尾 茂人（大阪大学）
JSMBE-MBI2015-06

シンポジウム「生体医用画像の新潮流 II」

Medical Imaging in Sport Science: From Segmental-motion Analysis toward
Anatomical Joint-motion Analysis in Biomechanics
近田 彰治（大阪大学）
JSMBE-MBI2015-07

シンポジウム「生体医用画像の新潮流 II」

医用画像のための対話的領域分割法とその植物モデリングへの応用
井尻 敬（立命館大学）
JSMBE-MBI2015-08

シンポジウム「生体医用画像の新潮流 II」

Biomedical Images for Simulation and Analysis of Surgical Procedures
中尾 恵 （京都大学）

JSMBE-MBI2015-09

A study on sparse shape modeling for fibular transfer planning in mandibular
reconstruction
Riho Kawasaki (Kyoto University), Megimi Nakao (Kyoto University), Yuichiro Imai (Rakuwakai
Otowa Hospital), Nobuhiro Ueda (Nara Medical University), Toshihide Hatanaka (Nara Medical
University), Mao Shiba (Nara Medical University), Tadaaki Kirita (Nara Medical University), Tetsuya
Matsuda (Kyoto University)
JSMBE-MBI2015-10

近赤外発光とカソードルミネッセンスを用いた生体マルチスケールイメージング
福島 昌一郎（大阪大学），古川 太一（大阪大学ナノ），新岡 宏彦（大阪大学），一宮 正義（大阪大学，滋賀県立大学），三宮 工（東京工業大学），芦田 昌明（大阪大学），三宅 淳（大阪大学），荒木 勉（大阪大学），橋本 守（大阪大学）
JSMBE-MBI2015-11

子宮頸がん放射線治療のための生体力学を統合した非剛体位置合わせの提案
ナディファ アユニサ，黒田 嘉宏（大阪大学），角谷 倫之（東北大学），吉元 俊輔，大城 理（大阪大学）
JSMBE-MBI2015-12

病理検体デジタルコンテンツ化プロジェクト
原口 亮，植木 渉，森田 佳明，松山 高明，植田 初江，中田 はる佳（国立循環器病研究センター），瀬尾 拡史（サイアメント）

Tomographic Micro-Diagnosis Using Multi-Functional OCT:
Tissue Classification Imaging of Atherosclerotic Plaque Using
ARV-OCT
Daisuke FURUKAWA*, Souichi SAEKI*, Takafumi HIRO**, and Masunori MATSUZAKI***
Graduate School of Engineering, Mechanical & Physical Engineering, Osaka City University, Osaka, Japan.
*** Department of Medicine, Nihon University, Tokyo, Japan.
*** Applied Medical Engineering Science, Graduate School of Medicine, Yamaguchi University

Abstract

Optical Coherence Tomography (OCT), based on a low coherence interferometry, has been being improved
as a 3-dimensional and tomographic subsurface imaging of micro structural biological tissue with highresolution of 1 to 10μm. OCT can provide morphological tissue structure as scattering intensity profile
generated from spatial variation of refractive index. Recently, “Multi-Functional OCT (MF-OCT)” has been
proposed as a promising in/ex vivo and in site clinical diagnostic modality. This can employ multi-modal
optical biopsy of functional information at the micrometer scale, bio-mechanical properties by OCSA; 3dimenational strain, blood velocity by OCDV; Doppler frequency by flow, bio-chemical composition by
OCM and OCD; water content and delivered drug infiltration, tissue characterization of fibrous/lipid tissue
by ARV, respectively.
In this paper, we propose a new atherosclerosis diagnosing technique using “Attenuation, Reflection and
Variance” (ARV-OCT). The tissue discrimination of fibrous and lipid is crucial to diagnose atherosclerotic
plaque predisposed to rupture. “ARV” can offer tissue characterization of fibrous and lipid by harnessing
optical information interacted with complicated biological tissue in OCT interference signals, i.e. attenuation,reflection, variance. Three optical parameters are extracted with almost the same high resolution as thatdefined by OCT system, by means of weighted moving least square method. The validation study was
experimentally carried out using atherosclerotic plaque specimen of WHHL rabbits. The optical signals
extracted from OCT image, which are correlated cross-sectionally with manually discriminated region offibrous/lipid tissue in histological images by medical histologists, can make a calibrated 3-dimensional state
map with respect to attenuation, reflection and variance. Comparing with this state map, the tissue-mixing
ratio between fibrous and lipid is calculated locally using k-means clustering algorithm. Consequently, theatherosclerotic tissue distribution classified into fibrous/lipid tissue could be visualized as a tomographic
color map of tissue mixing ratio. High efficacy of ARV was in/ex vivo demonstrated clinically in terms ofatherosclerosis assessments, that is, vulnerable characteristics of unstable plaque predisposed to rupture.

Keywords: Optical Coherence Tomography, ARV-OCT, Tissue Characterization, Biomedical Imaging, Atherosclerotic
Plaque6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 2016/3

Proceedings of Medical and Biological Imaging - JSMBE 2016/3

JSMBE-MBI2015-02 (2016-03)

Segmentation of Surgical Instruments from RGB-D Endoscopic
Images using Convolutional Neural Networks: Preliminary
Experiments towards Quantitative Skill Assessment

Yuta Hiasa*, Yuki Suzuki*, Austin Reiter**, Yoshito Otake*, Masayasu Nishi***, Hitoshi Harada***, Koki Koyama*,
Shingo Kanaji***, Yoshihiro Kakeji***, Yoshinobu Sato**Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan.
**Department of a Computer Science, Johns Hopkins University, Baltimore MD, USA.
***Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Japan.

Abstract
Manipulation of the endoscopic instruments is a challenging task and its quantitative and objective assessment is difficult.
Taking advantage of the recently released view-angle adjustable stereo endoscope that our hospital uses in clinical routine,
we aim to assess the surgical skill by comparing quantitative metrics such as the time for each manipulation, length of thetool tip trajectory and the number of errors during a routine surgery (not with training phantoms nor surgical simulators). In
order to achieve the goal, we attempted tracking of surgical instruments using the stereo endoscope images.
The algorithm of 3D tracking of forceps from a stereo endoscope video acquired by da Vinci system was publishedpreviously by Reiter et al. [1]. In general, tracking of the tool tip from a stereo video requires the following steps. (1) Stereo
camera calibration, (2) segmentation of the tool, (3) tool tip tracking, (4) 3D reconstruction of the tip. Among these steps,
segmentation is important that determines robustness of the algorithm, especially in a real surgical video containing morebackground noise such as surgical gauze and staples.
In this paper, we propose and evaluate a method for segmentation ofsurgical instruments using convolutional neural networks (CNN) [2].
The proposed network uses RGB and depth image that was calculated
from the stereo endoscope images and the output of the networks is alikelihood image (Fig. 1). In Fig.1, the white pixels indicate highprobability of instruments and the black pixels indicate highprobability of background. The training dataset is generated as follows.
A three dimensional instrument model is reconstructed from amanually segmented stereo image and then projected onto thebackground images with random perturbation in translation androtation in 3D. We compared the proposed algorithm with thepreviously proposed one using GMM [1]. The result shows that the
GMM method confused the gauze with the instrument (Fig. 1 thirdcolumn) and contained more noise as opposed to the correct andcleaner segmentation by the proposed method.
Future works include an extensive evaluation of the segmentation accuracy and comparison with other state-of-the-art
segmentation algorithms.
Keywords: Endoscopic skill assessment, Segmentation of surgical instruments, Convolutional Neural Networks.

Acknowledgements
This work was supported in part by JSPS KAKENHI No. 26108004 15K01330, and JST PRESTO.

References[1] Reiter, Austin, et al. "Appearance learning for 3d tracking of robotic surgical tools." The International Journal of
Robotics Research, vol.33, no.2, pp.342-356, 2014.
[2] Farabet, Clement, et al. "Learning hierarchical features for scene labeling." Pattern Analysis and Machine Intelligence,
IEEE Transactions on, vol.35, no.8, pp.1915-1929, 2013.

This article has been printed without reviewing and editing as received from the authors, and the copyright of the article
belongs to the authors.
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 2016/3
Proceedings of Medical and Biological Imaging - JSMBE 2016/3

JSMBE-MBI2015-03 (2016-03)

Biomechanical Structure and Haptic interaction
Yoshihiro Kuroda** Cybermedia Center, Osaka University, Toyonaka, Japan.

Abstract
A kind of haptic displays, which render touch sensation, and biomechanical simulation have been studied especially forforce feedback in tele-operation, and medical training and support. However, most existing devices are large and needs tobe fixed on the environment, which reduce mobility and workspace. In this talk, the presenter focuses on haptic interaction
studies using biomechanical structures of a human body to improve compactness and mobility of the device as well as
applicability of augmented reality situation. The presenter also introduces a biomechanical simulation study based on finite
element method to enable haptic interaction with reduced artifact in case of large deformation. Finally, the roles of medical
and biological imaging in this field are discussed.
Keywords: haptic interaction, biomechanical structure, finite element method.

This article has been printed without reviewing and editing as received from the authors, and the copyright of the article
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 2016/3
JSMBE-MBI2015-04 (2016-03)

Proceedings of Medical and Biological Imaging - JSMBE 2016/3

Application of hyperspectral imaging in medicine
Takashi Nagaoka** Department of Computational system Biology, Faculty of Biology-Oriented Science and Technology, Kinki University, Wakayama, Japan.

Abstract
Early detection and proper excision of the primary lesions of malignant melanoma are crucial for reducing
melanoma-related deaths. In order to support the early detection of melanoma, melanoma screening systems havebeen extensively studied and developed. Recently we have proposed a melanoma discrimination index derived
from hyperspectral data in the visible to near infrared wavelength region. The index represents variegation inspectra over a lesion and its peripheral normal skin. Performance of the system has been studied for 34 cases ofmelanoma, and 118 cases of non-melanoma. The index has achieved a sensitivity of 77 % and a specificity of 85 %for the population.
Keywords: hyperspectral imaging, spectral diagnosis, melanoma, cosmetics.

Figure 1. Hyperspectral imager for medical use

Figure 2. Melanoma discrimination index

References[1] T. Nagaoka, A. Nakamura, H. Okutani, Y. Kiyohara, and T. Sota, “A possible melanoma discrimination index
based on hyperspectral data: a pilot study”, Skin Research and Technology, vol. 19, no. 1, pp. e290-e296, 2011[2] T. Nagaoka, A. Nakamura, H. Okutani, Y. Kiyohara, H. Koga, T. Saida, and T. Sota, “Hyperspectroscopic
screening of melanoma on acral volar skin”, Skin Research and Technology, vol. 18, no. 3, pp. 301-310, 2012

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Medical and Biological Imaging for Bioscience
Shigeto Seno** Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan.

Abstract
Bioimaging is a technique used to analyze the protein dynamics and molecular distribution and localization by capturing images at the individual, tissue, or cellular level. With advances in optical techniques (e.g. high-resolution microscopy) and genetic engineering techniques (e.g. fluorescent protein and genome recombination), bioimaging has become important in the advancement of bioscience research. Cells and particles can be observed using methods such asphase-contrast or differential interference microscopy or using confocal laser scanning microscopy with fluorescently-labelled samples. Multiphoton excitation microscopy is also used for intravital cellular or protein live-imaging observations. These sophisticated forms of microscopy can concurrently capture images over fixed intervals from multiple
fields to produce time-lapse images for the observation of long-term changes. Meanwhile, High-resolution andhigh-throughput bioimaging data capture has made the development of techniques for automatic cell tracking and statistical analysis an urgent issue. Here, I introduce a case study of analyzing multichannel imaging data in which the cell cycle
was visualized using fluorescent proteins. In this case, automatic cell tracking was implemented based on particle filter,and quantitative analysis of drug efficacy was conducted.
Keywords: bioimaging, cell tracking, particle filter, quantitative analysis.

Figure 1. Visualization of the cell cycle using the Fucci system and tracking cells using particle filter (a). A tracking result of cell positions and cell cycle phases (b).

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 2016/3
Proceedings of Medical and Biological Imaging - JSMBE 2016/3

JSMBE-MBI2015-06 (2016-03)

Medical Imaging in Sport Science: From Segmental-motion
Analysis toward Anatomical Joint-motion Analysis in Biomechanics
Shoji Konda*, *** Research Fellow of Japan Society for the Promotion of Science.
** Department of Orthopaedic Biomaterial Science, Graduate School of Medicine, Osaka University, Osaka, Japan.

Abstract
Biomechanics in sports is the science concerned with the internal and external forces acting on a human body and effects produced by these forces, aiming to enhance performance outcome and to prevent injuries in sports [1]. In thispresentation, I introduce an overview of biomechanics in sports in past, present, and future, using biomechanical analysis
of baseball pitching as an example. Baseball pitching is a typical example of sports technique that rapidly accelerates the
distal end of the limb to produce a high-velocity outcome (more than 150 km/h). Many baseball pitchers have experienced
shoulder injury. However, the biomechanical cause of shoulder injury have been hypothesized by orthopaedic surgeons
and physical therapists based on their clinical experiences without a biomechanical knowledge of anatomical joint-motion
recorded in the actual baseball pitching.
A motion of scapula, a trigonal bone locating behind the shoulder, has been clinically hypothesized as an importantfactor associating with a risk of shoulder injury. In past, a motion of baseball pitching has been filled by a high-speed
video camcorder, and then a rigid-body link-segment model has been adopted to analyze the human movement [2]. Thismodel assumed that (1) body parts (torso and upper-limb) are rigid-segments, and (2) the rigid-segments are connected bya simplified mechanical connection (ball-and-socket joints), named “segmental-motion analysis”. However, this modelcannot test the clinical hypothesis about the scapular motion because the scapula is assumed to be in the simplified torso-cuboid. We recoded the 3D scapular motion during the actual baseball pitching using the small sensor attached to theskin overlying the scapula [3], named “skeletal motion analysis”. The 3D skeletal-motion of the shoulder has been described, however, the shoulder injury are resulted from an interaction between the skeletal-motion and anatomical morphology of the bone and soft-tissues surrounding the joint. We have aimed to determine the cause of shoulder injury by a
combination of the skeletal-motion analysis and the subject-specific morphology of the bone and soft-tissues acquired by
the medical imaging analysis, named “anatomical joint-motion analysis”. I’d like to discuss about an application of the
medical imaging analysis to the biomechanics in sports.

Keywords: rigid-body link-segment model, skeletal model, anatomical morphology, shoulder, scapula,
References[1] Hay JG: The biomechanics of sports techniques, 4th ed. Prentice Hall, Upper Saddle River, pp.1-8, 1985.
[2] Feltner ME, Dapena J: Dynamics of the Shoulder and Elbow Joints of the Throwing Arm During a Baseball Pitch. Int
J Sport Biomech. 2(4), pp. 235-59, 1986.
[3] Konda S, Yanai T, Sakurai S: Configuration of the Shoulder Complex During the Arm-Cocking Phase in BaseballPitching. Am J Sports Med. 43(10), pp. 2445-51, 2015

This article has been printed without reviewing and editing as received from the authors, and the copyright of the article
belongs to the authors.
11

Interactive techniques for medical volume segmentation andtheir application to flower modelingTakashi Ijiri, Department of Media Technology, Ritsumeikan University, Shiga, Japan.

Abstract
In this talk, we present our previous studies on interactive tools for volume segmentation [1] and a technique for modeling flowers by using X-ray computed tomography (CT) [2].
Interactive volume segmentation [1]. Volume segmentation is a time-consuming task, especially when a region of interest (ROI) contains ambiguous boundaries. We present a contour-based segmentation tool in which the user places multiple contours in the three-dimensional (3D) space. Given the multiple contours, our system constructs a boundary surface
that passes through all the contours and fits to edges of the input volume. Since our contour-based interface allows theuser to specify the boundary of a ROI directly, ROIs with ambiguous boundaries could be extracted accurately.
Flower modeling by using X-ray CT [2]. Reconstructing 3D models of flowers is an important topic in the field ofcomputer graphics. It is however difficult to apply common 3D reconstruction methods (e.g., image-based reconstructions
or range scanners) to flowers, since flowers often contain highly occluded structures. In this study, we adopt an X-ray CT;
we first capture volumes of sample flowers and reconstruct their 3D shapes by using the volumes as guides. To reconstruct flower components, such as pistils, stamens, petals and sepals, we present a novel active contour and active surface
model that semi-automatically fit to flower components in a CT volume. Our technique achieved to reconstruct highly
realistic flower models.
Keywords: interactive segmentation, volume segmentation, shape modeling.

References[1] Takashi Ijiri, Shin Yoshizawa, Yu Sato, Masaaki Ito, and Hideo Yokota: Bilateral Hermite Radial Basis
Functions for Contour-based Volume Segmentation. Computer Graphics Forum, 32(2), 123-132, May
2013.
[2] Takashi Ijiri, Shin Yoshizawa, Hideo Yokota, Takeo Igarashi. Flower Modeling via X-ray Computed Tomography, ACM Trans. Graph. 33(4), 48, July 2014.

12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 2016/3
Proceedings of Medical and Biological Imaging - JSMBE 2016/3

JSMBE-MBI2015-08 (2016-03)

Biomedical Images for Simulation and Analysis of Surgical Procedures
Megumi Nakao * and Tetsuya Matsuda ** Graduate School of Informatics, Kyoto University, Sakyo, Kyoto, Japan.

Abstract
Virtual planning and preoperative simulation using computed tomography (CT) or magnetic resonance (MR) images
enable quantitative, strategic planning of patient-specific surgical procedures. To perform evidence-based surgery and standardization of surgical procedures, the interest for an image-based estimator is increasing among surgeons and researchers.
Statistical analysis of the surgical planning data and investigation of implicit factors affecting the decision-making are also
important for designing next-generation planning/navigation system.
In this presentation, we will introduce our recent study on surgical process modeling and analysis using medical images.
The concept of deformable resection process map [1] for tumor resection in abdominal/thoracic surgery is first introduced.
(see Figure 1) Second, a user experiment to quantitatively analyze surgical process and decision-making in mandibular
reconstructive surgery [2] is reported. Importance of simulation as a tool for coupling medical knowledge to machine learning will be discussed.
Keywords: Surgical process analysis, semi-automatic planning

Fig. 1 Deformable resection process map as an intraoperative guide.
[1] 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.
[2] M. Nakao, M. Hosokawa, Y. Imai, N. Ueda, T. Hatanaka, T. Kirita and T. Matsuda, "Volumetric fibular transfer planningwith shape-based indicators in mandibular reconstruction", IEEE Journal of Biomedical and Health Informatics, Vol. 19,
No.2, pp.581-589, 2015.

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belongs to the authors.
13

A Study on Sparse Shape Modeling for Fibular Transfer Planningin Mandibular Reconstruction
Riho Kawasaki*, Megumi Nakao*, Yuichiro Imai**, Nobuhiro Ueda***, Toshihide Hatanaka***, Mao Shiba***,Tadaaki Kirita***, Tetsuya Matsuda** Graduate School of Informatics, Kyoto University, 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
Preoperative planning using computer-aided design with 3D CT data is an active area of research. For mandibular reconstruction with fibular segments, it is necessary to determine osteotomy lines for fibular shapes and the precise placement offibular segments in the mandible, but recent interactive planning software [1] cannot secure objectivity of the planning andtime-consuming trial-and-error processes are required.
In this study, we propose an automated preoperative planning method that estimates a plan fitted to the data of a newpatient using a planned dataset of previous patients. The proposed method introduces sparse shape modeling using sparseshape composition [2]. In this modeling, we select a subset of the data from a prepared preoperative planning dataset tomake an example or instance of reconstruction via a linear combination of the data. We conduct experiments using thedataset planned by medical doctors and compare the instance estimated by the proposed method to the manual placementby these doctors.
Keywords: machine learning, mandibular reconstruction, computer assisted surgery.

Figure 1. Comparison of fibular transfer planning results for (a) the manual placement given by surgeons, (b) the proposed sparse modeling, (c) the non-sparse modeling, and (d) the best-fit training data.
References[1] M. Nakao, M. Hosokawa, Y. Imai, N. Ueda, T. Hatanaka, T. Kirita and T. Matsuda, “Volumetric fibular transfer planningwith shape-based indicators in mandibular reconstruction,” IEEE Journal of Biomedical and Informatics, Vol. 19, No. 2,
pp. 581-589, 2015.
[2] S. Zhang, Y. Zhan, M. Dewan, J. Huang, D. N. Metaxas, and X. S. Zhou, “Towards robust and effective shape modeling:Sparse Shape Composition,” Medical Image Analysis, Vol. 16, No. 1, pp. 265-277, 2012.

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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 2016/3
Proceedings of Medical and Biological Imaging - JSMBE 2016/3

JSMBE-MBI2015-10 (2016-03)

Multi-scaled bioimaging with near-infrared luminescence andcathodoluminescence
Shoichiro Fukushima*, Taichi Furukawa**, Hirohiko Niioka*, Masayoshi Ichimiya*, ***, Takumi Sannomiya****,
Masaaki Ashida*, Jun Miyake*, Tsutomu Araki*, and Mamoru Hashimoto** Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, Japan.
** Institute for NanoScience Design, Osaka University, Toyonaka, Osaka, Japan.
*** School of Engineering, The University of Shiga Prefecture, Hikone, Shiga, Japan.
****Department of Innovative and Engineered Materials, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan.

Abstract
Correlative light and electron microscopy (CLEM) is an emerging technique which combines the advantages of these
two microscopies to observe the same region of interest. Light microscopy (LM) visualizes cellular components and protein distributions using fluorescent probes of different colors. Since the spatial resolution of LM is restricted by the diffraction limit of light, the positions of proteins cannot be resolved precisely. Electron microscopy (EM) plays an importantrole in identifying the protein distributions and visualizing ultra-structures of cellular components. Due to its use of anelectron beam, EM has higher spatial resolution compared with conventional LM. CLEM overcomes the limitations of theindividual microscopies and combines colored imaging and high spatial resolution imaging of a sample.
Although CLEM technique keeps advantages of LM and EM, there are still some drawbacks of both microscopies. LM
observation is suffered its imaging depth due to the scattering and adsorption of incident light by water and intrinsic tissue
molecules. Multiphoton fluorescence microscopy is known as its high penetration depth of incident light; however, theimaging depth is limited around a few hundred micrometers to one millimeter. Meanwhile, EM observation has difficulties to observe multiple kinds of proteins because EM images are monochrome.
Near-infrared (NIR) microscopy and CL microscopy have possibility to enhance the conventional CLEM technique including deep observation and recognition of kinds of molecules. Using NIR wavelength region for both excitation lightand emission light enables deep observation in biological samples. In addition, higher spatial resolution compared with
LM is expected by CL microscopy due to the excitation with electron beam. CL microscopy remains the advantage ofcolored imaging and kinds of targets can be recognized by emission wavelength of CL. To realize correlative NIRL and CL
imaging, rare-earth oxide Y2O3: Tm, Yb and Y2O3: Er, Yb nanophosphors (NPs) were developed. Luminescent properties of theNPs under excitation with 980 nm NIR light and electron beam were reported. Multiscale NIR luminescence/CL bioimaging
with the NPs was demonstrated through in vivo and in vitro NIR deep-tissue observations, cellular NIR luminescence imaging,and high-spatial resolution/multi-colored cellular CL imaging.

Keywords: up-conversion, near-infrared imaging, cathodoluminescence, rare-earth doped materials.

This article has been printed without reviewing and editing as received from the authors, and the copyright of the article
belongs to the authors.
15

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 2016/3
Proceedings of Medical and Biological Imaging - JSMBE 2016/3

JSMBE-MBI2015-11 (2016-03)

A Proposal on Biomechanically-combined Deformable Image
Registration for Cervical Cancer Radiotherapy
Nadhifa Ayunisa1, Yoshihiro Kuroda2, Noriyuki Kadoya3, Shunsuke Yoshimoto1, Osamu Oshiro1
1) Graduate School of Engineering Science, Osaka University
2) Cybermedia Center, Osaka University3) Department of Radiation Oncology, Tohoku University School of Medicine

Abstract
Image registration is one of image processing techniques that are fundamental to medical image interpretation and analysis. Various methods in deformable (non-rigid) image registration have been employed to find an optimum solution forclinical cases, however previous methods are not intended to solve organ deformation caused by physical intervention, suchas the case happened during cervical cancer radiotherapy. During brachytherapy, target organ (uterus) is deformed by insertion of applicator. This results in difficulties when performing dose calculation together with images from external beam
radiotherapy images, in which uterus does not receive intervention by tool.
Taking the physical intervention characteristic into account, this study proposes a method to solve image matching forimages taken during radiotherapy for cervical cancer. The proposed deformable image registration method combines image
registration based on intensity and physical modeling of target organ. The location of applicator and internal wall of uterus
in images are used as corresponding points to match uterus structures with finite element method (FEM). Displacement
field obtained from this process is then compared to the transformation field from parametric intensity registration, and thisprocess is used as a penalty term when combined with the cost function in intensity image registration. The new term
provides correction in the viewpoint of biomechanical simulation that is expected to minimize error in image matching justbased on intensity.
In the experiment, CT images of woman’s pelvis phantom are used. Material properties that are used in physical simulation are typical values of urethane, which is the material uterus phantom is made of. The resultant image based on combined
method is evaluated by comparing position of markers in it with the result of image registration based on only intensity to
see the contribution of biomechanical part in improving registration accuracy.
Keywords: deformable image registration, finite element method, uterus, radiotherapy

This article has been printed without reviewing and editing as received from the authors, and the copyright of the article
belongs to the authors.
16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 2016/3
Proceedings of Medical and Biological Imaging - JSMBE 2016/3

JSMBE-MBI2015-12 (2016-03)

Computational Cardiac Modeling Project using Human Specimens
Ryo Haraguchi1), Wataru Ueki2), Yoshiaki Morita2), Taka-aki Matsuyama3), Hatsue Ishibashi-Ueda3), Haruka Nakada4),
Hirofumi Seo5)
1)
2)
3)
4)
5)

Department of Medical Informatics, National Cerebral and Cardiovascular Center, Suita, Japan.
Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Japan.
Department of Pathology, National Cerebral and Cardiovascular Center, Suita, Japan.
Department of Intelligent Asset Management, National Cerebral and Cardiovascular Center, Suita, Japan.
SCIEMENT, Inc.

Abstract
Vast macroscopic and histological specimens on cardiovascular diseases have been saved since its founding in National
Cerebral and Cardiovascular Center. We conduct the project for contributing medical research and education by digitizing
those specimens and developing novel computational cardiac models. We consider that the digitalization of histological
specimens improves the accessibility and raise the value of them. In this presentation, we introduce the progress of our
project.
Keywords: computational cardiac model, human specimens.
1This article has been printed without reviewing and editing as received from the authors, and the copyright of the article
belongs to the authors.
17