The 4th Conference on Medical and Biological Imaging
Program & Abstract
March 25, 2017
Sigma Hall, Osaka University

生体医用画像研究会 第4回若手発表会
プログラム・抄録集

2017年3月25日
大阪大学 基礎工学国際棟

- CONTENTSJSMBE-MBI2016-01

Segmentation of stereo endoscopic video images using super-pixel and disparity map inlaparoscopic surgery
Satoshi Nakatani, Yuta Hiasa, Yoshito Otake (NAIST), Hitoshi Harada, Shingo Kanaji,
Yoshihiro Kakeji (Kobe University), Yoshinobu Sato (NAIST)

JSMBE-MBI2016-02

Super pixels based deformation recognition for endoscopic images
Shuqiong Wu, Megumi Nakao, and Tetsuya Matsuda (Kyoto University)

JSMBE-MBI2016-03

The utilization of multidetector computed tomography (MDCT) for Simulation-based
training program for minimally invasive cardiac surgery (MICS)
Daisuke Yasumizu, Takashi Murakami, Hiromich Fujii, Masanori Sakaguchi, Yosuke Takahashi,
Shinsuke Nishimura, Yoshito Sakon, Toshihiko Shibata (Osaka City University)

JSMBE-MBI2016-04

Viscoelastic simulation based on facial motion measurement

Hiroki Kato, Yoshihiro Kuroda, Chihiro Tanikawa, Shunsuke Yoshimoto, Osamu Oshiro (Osaka
University), Kenji Takada (National University of Singapore)

JSMBE-MBI2016-05[Invited Lecture] Structure of soft biological tissues based on viscoelastic propeties
Yo Kobayashi (Osaka University)

JSMBE-MBI2016-06 (Symposium "New Trends in Medical and Biological Imaging Part III")[Symposium] Applications of Deep Convolutional Neural Networks for Analysis of
Pathological Images

Naoaki Ono, Victor Andrew Antonio (NAIST), Akira Saito (Univerity of Tokyo), Tetsuo Sato (Gunma
Prefectural College of Health Sciences), Md. Altaf-Ul-Amin, Shigehiko Kanaya (NAIST)

JSMBE-MBI2016-07 (Symposium "New Trends in Medical and Biological Imaging Part III")[Symposium] Clustering Biomedical Information by Deep Learning
Seiichi Tagawa, Jun Miyake (Osaka University)

JSMBE-MBI2016-08 (Symposium "New Trends in Medical and Biological Imaging Part III")[Symposium] Progress on Deep Learning in Computer Vision and its connection with
Medical Image Computing
Yuji Oyamada (Tottori University)

JSMBE-MBI2016-09 (Symposium "New Trends in Medical and Biological Imaging Part III")[Symposium] Soft Computing in Medical Image Processing
Syoji Kobashi (University of Hyogo)

JSMBE-MBI2016-10 (Symposium "New Trends in Medical and Biological Imaging Part III")[Symposium] Medical-Engineering collaboration and Biomedical imaging research

Tatsuhiko Arafune, Yuki Nakano (Tokyo Denki University), Shinji Chiba (Microsoft Japan),
Takashi Suzuki (Tokyo Women's Medical University), Toshikatsu Washio (AIST), Tomoyuki Yano
(Gent University)

JSMBE-MBI2016-11

Evaluation of machine learning classifier for glaucomatous optic nerve head structureusing optical coherence tomography images

Guangzhou An (Topcon Corporation, RIKEN), Kazuko Omodaka, Kazuko Omodaka, Satoru Tsuda,
Yukihiro Shiga, Naoko Takada (Tohoku University), Tsutomu Kikawa (Topcon Corporation), Toru
Nakazawa (Tohoku University), Hideo Yokota (RIKEN), Masahiro Akiba (Topcon Corporation,
RIKEN)

JSMBE-MBI2016-12

Partial region learning in CNN and majority voting for breast lesion classification in
mammograms

Kaihei Kitamura, Ryo Takeuchi, Masahito Aoyama, Hisashi Fujiwara, Yoshitaka Masutani (Hiroshima
City University)

JSMBE-MBI2016-13

Multi-class diagnosis for Parkinsonism with support vector machine using DaTSCANSPECT images

Shogo Watanabe, Yuichi Kimura (Kindai University), Toshiyuki Hayashi, Masahiro Mishina (Nippon
Medical School)

JSMBE-MBI2016-14

Stability analysis of a dynamical system describing competition for multi-valued
discrete tomography
Takeshi Kojima, Tetsushi Ueta, Tetsuya Yoshinaga (Tokushima University)

JSMBE-MBI2016-15

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

JSMBE-MBI2016-16

Database construction of pre-postoperative musculoskeletal and implants towards
statistical modeling in total hip arthroplasty

Kazuma Suenaga, Koki Koyama, Yuki Nakanishi, Futoshi Yokota, Yoshito Otake (NAIST),
Masaki Takao, Takeshi Ogawa, Keisuke Uemura, Nobuhiko Sugao (Osaka University),Yoshinobu Sato
(NAIST)̶目次̶
JSMBE-MBI2016-01

スーパーピクセルと視差情報を用いた腹腔鏡下ステレオ内視鏡画像のセグメン
テーション
中谷 聡志，日朝 祐太，大竹 義人（奈良先端科学技術大学院大学），原田 仁，金治 新悟，掛地 吉弘（神戸大学），佐藤 嘉伸（奈良先端科学技術大学院大学）
JSMBE-MBI2016-02

Super pixels based deformation recognition for endoscopic images
Shuqiong Wu, Megumi Nakao, and Tetsuya Matsuda (Kyoto University)

JSMBE-MBI2016-03

小切開心臓手術 simulation のための CT データの活用

安水 大介，村上 貴志，藤井 弘通，阪口 正則，高橋 洋介，西村 慎亮，左近 慶人，
柴田 利彦（大阪市立大学）

JSMBE-MBI2016-04

顔動作計測に基づく粘弾性シミュレーション
加藤 弘樹 (大阪大学), 黒田 嘉宏 (大阪大学), 谷川 千尋 (大阪大学), 吉元 俊輔 (大阪大学),
大城 理 (大阪大学), 高田 健治 (シンガポール国立大学)

JSMBE-MBI2016-05[特別講演] 物性から考察する生体軟組織の構造小林 洋（大阪大学）

JSMBE-MBI2016-06

シンポジウム「生体医用画像の新潮流 III」

バイオインフォマティクスと生体医用画像

小野 直亮（奈良先端科学技術大学院大学）, Akira Saito（東京大学），Tetsuo Sato （群馬県立県民健康科学大学）, Md. Altaf-Ul-Amin, Shigehiko Kanaya（奈良先端科学技術大学院大学）
JSMBE-MBI2016-07

シンポジウム「生体医用画像の新潮流 III」

深層学習による生体情報分類

田川 聖一，三宅 淳（大阪大学）
JSMBE-MBI2016-08

シンポジウム「生体医用画像の新潮流 III」

コンピュータビジョンと生体医用画像
小山田 雄仁（鳥取大学）
JSMBE-MBI2016-09

シンポジウム「生体医用画像の新潮流 III」

ソフトコンピューティングと生体医用画像
小橋 昌司（兵庫県立大学）

JSMBE-MBI2016-10

シンポジウム「生体医用画像の新潮流 III」

医工連携と生体医用画像

荒船 龍彦，中野 祐樹（東京電機大学），千葉 慎二（日本マイクロソフト）, 鈴木 孝司（東京女子医大）, 鷲尾 利克（産業技術総合研究所），Tomoyuki Yano (Gent University)
JSMBE-MBI2016-11

Evaluation of machine learning classifier for glaucomatous optic nerve head structureusing optical coherence tomography images

Guangzhou An (Topcon Corporation, RIKEN), Kazuko Omodaka, Kazuko Omodaka, Satoru Tsuda,
Yukihiro Shiga, Naoko Takada (Tohoku University), Tsutomu Kikawa (Topcon Corporation), Toru
Nakazawa (Tohoku University), Hideo Yokota (RIKEN), Masahiro Akiba (Topcon Corporation,
RIKEN)

JSMBE-MBI2016-12

乳房 X 線画像における病変の良悪性鑑別のための CNN による部分領域学習と多数決処理
北村 凱平，竹内 遼，青山 正人，藤原 久志，増谷 佳孝（広島市立大学）
JSMBE-MBI2016-13

DaTSCAN SPECT 画像を用いたサポートベクターマシンによるパーキンソニズ
ムの多群鑑別渡辺 翔吾，木村 裕一（近畿大学），林 俊行，三品 雅洋（日本医科大学）
JSMBE-MBI2016-14

Stability analysis of a dynamical system describing competition for multi-valued
discrete tomography兒島 雄志, 上田 哲史, 吉永 哲哉（徳島大学）

JSMBE-MBI2016-15

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（大阪大学）、
加藤 博一（奈良先端科学技術大学院大学）、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-MBI2016-16

人工股関節全置換術における術前術後の筋骨格及びインプラントの統計モデル作成を目的としたデータベース構築
末長 和馬，児山 昴生，中西 裕紀，横田 太，大竹 義人（奈良先端科学技術大学院大学），
高尾 正樹，小川 剛，上村 圭亮，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）

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-01 (2017-03)

Segmentation of stereo endoscopic video images using super-pixel
and disparity map in laparoscopic surgery
Satoshi Nakatani*,Yuta Hiasa*,Yoshito Otake*, Hitoshi Harada**, Shingo Kanaji**, Yoshihiro Kakeji**, Yoshinobu Sato** Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan** Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Japan.

Abstract
Gastrectomy with lymph node dissection is a standard of laparoscopic surgery and improves the survival rate of gastriccancer patients. During lymph node dissection, surgeons remove the fat that contains the lymph nodes. Since the pancreas
is covered with the fat and also image features of the fat and pancreas are similar, estimating their boundary from visualcues is difficult for novice surgeons. On the other hand, in recent years stereo endoscopes, which allow binocular disparity,
have become widespread. In this paper, we propose a method for automatic tracking of multiple organs in the stereo endoscopic images using image and depth features. Masoud et al [1] estimated three-dimensional shape of non-rigidly deforming
organs in endoscopic images using image features and a CT image. The method first estimates the likelihood of each pixel
being inside a specific organ, then three dimensional reconstruction is performed. Bodenstedt et al [2] also used random
forest for structure classification in laparoscopic surgery, but they employed the super-pixel algorithm as a preprocessing to
improve classification accuracy.
In this paper, we improve Bodenstedt’s method by adding the depth feature derived from the stereo endoscope and graphcut algorithm as in Yang et al [3]. In experiments, three-class classifications (surgical instruments, pancreas and background)
were performed on real surgery videos by the previously method [2] and the proposed method. Two video sequences (150frames in length), close-up and zoom-out views, extracted from a lymph node dissection surgery were used. The random
forest was trained with the first 10 frames and tested with the remaining 140 frames. An example of experimental results isshown in Fig 1. Following the experiments in [2], we employed DICE coefficient as a performance metric. As a result, theaverage DICE coefficient of the two sequences using the previous and proposed method was 0.725 and 0.777, respectively.
Our future work includes improving the feature vector and increasing the number of patients in the training dataset which
may eliminate the need for the patient-specific training by searching a similar sequences from the large database.

(a) Input Image(b) Ground truth(c) Previous method (d) Proposed method(e) DICE coefficientFig 1. Example segmentation results (green: surgical instruments, red: pancreas). (a) Input image, (b) ground truth segmentation provided by an expert surgeon. The segmentation result by the previous method (c), and the proposed method (d),(e) DICE coefficient of proposed method as a function of time.
Keywords: Stereo endoscopic image segmentation, super-pixel classification, disparity map, laparoscopic surgery
References[1] Masoud S. Nosrati, et al. “Simultaneous multi-structure segmentation and 3D nonrigid pose estimation in imageguided robotic surgery”, IEEE transactions on medical imaging, 2016.
[2] Bodenstedt, Sebastian, et al. "Superpixel-based structure classification for laparoscopic surgery." SPIE Medical Imaging. International Society for Optics and Photonics, 2016.
[3] Yang, Jingyu, et al. “Graph-based segmentation for rgb-d data using 3-d geometry enhanced superpixels." IEEE
transactions on cybernetics 45.5 (2015): 927-940.

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

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-02 (2017-03)

Super pixels based deformation recognition for endoscopic images
Shuqiong Wu*, Megumi Nakao*, and Tetsuya Matsuda** Graduate School of Informatics, Kyoto University, Sakyo, Kyoto, Japan.

Abstract
Deformation recognition of intra-operative organs, which means that each region of the organ can be tracked during thesurgery even the organ is deformed, is especially necessary for locating tumor positions and planning resection maps in asurgery. Many researchers realized deformation recognition using 3D models deduced from pre-operative Computed tomography volume Data. For example, Nakao and Minato utilized pre-operative 3D model to locate tumor positions [1].
However, for organs such as the lungs, which are inflated before surgery but deflated during surgery, it is difficult to provide
precise information by only using the pre-operative data. To improve this kind of 3D models, our research focuses on
tracking each small region of the lung surface from intra-operative endoscopic images.
In recent year, super pixels are widely used in medical image processing since it can divide the whole image into small
region in which all pixels are nearly homogeneous [2]. The boundary of each super pixel represents the dividing line between two different groups of homogeneous pixels. When deformation of the organ happens, its appearance and shape arechanged. Thus, tracking techniques such as SIFT, SURF, Mean-shift fails in such a situation. By contrast, the boundaries ofsuper pixels are more robust against deformation because the local contrast around the dividing line still exists even theorgan is deformed. If we can track the boundary of each super pixel, the small region tracking of the organ can be achieved.
This presentation will mainly introduce how to calculate the boundary of each super pixel, and how to track these boundaries
using local contrast. Figure 1 shows an example of the super pixel boundary tracking. As the red arrows show, the super
pixel is tracked even it is deformed.
Keywords: Deformation, Lung, Super pixel

Fig. 1 An example of the super pixel boundary tracking[1] M. Nakao and K. Minato, “Physics-based Interactive Volume Manipulation for Sharing Surgical Process”, IEEE Transactions on Information Technology in Biomedicine, Vol. 14, No. 3, pp. 809-816, 2010.
[2] M. Bergh, X. Boix, G. Roig, and L. Gool, “SEEDS: Superpixels Extracted via Energy-Driven Sampling”, International
Journal of Computer Vision (IJCV), Vol.111, No.3, pp.298-314, 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.

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-03 (2017-03)

The utilization of multidetector computed tomography (MDCT) for Simulation-based training program forminimally invasive cardiac surgery (MICS)
Daisuke Yasumizu, Takashi Murakami, Hiromich Fujii, Masanori Sakaguchi, Yosuke Takahashi, Shinsuke Nishimura,
Yoshito Sakon, Toshihiko Shibata
Department of Cardiovascular surgery, Osaka City University, Osaka, Japan.

Abstract
Minimally invasive cardiac surgery has been widely adopted recently. However, it takes an enormous amount of timeto acquire this skill because of its difficulty and the limited number of the operation. The marked progress of medical devices like MDCT is so tremendous that we can visualize stereoscopic human anatomy in detail. As a result, we could
make patient-specific replicas. We have introduced the simulation-based training program for MICS to assure its skill
effectively. In this presentation, we will introduce our new surgical simulator using a fusion of the Patient-specific modeland technology of AR (Augmented Reality). We have ever made the silicon replica models manufactured by a 3D printerfrom the images obtained by MDCT. Segmentation was performed by CT workstation (Vincent, Fuji Film). We could
successfully perform MICS with no events by this simulator. It ultimately led to a complete planning of the operation because we could try various techniques, and to optimize procedural details regarding the direction of suture traction, exposure approaches, the order of stitches, coordination, and arrangement of the procedures. Moreover, had the opportunity to
train the use of knot pusher in the limited operative field. But the problem of using patient-specific model is the cost of
making the model. So we created and output quite a new simulation model using AR engine (Unity, Unity and Vuforia,
PTC) to solve these problems. It reflects the body type's difference to output the skin and thoracic cage data. Our simulation-based training is useful to contribute to the safety and smoothness of the operations. Various technologies can help
standardize the operative technique about MICS through visualization and simulation.

Figure 1.our new surgical simulator using a fusion of
the Patient-specific model and technology of AR (Augmented Reality)
Keywords: Simulation-based training, minimally invasive cardiac surgery (MICS),This article has been printed without reviewing and editing as received from the authors, and the copyright of the article
belongs to the authors.

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-04 (2017-03)

Viscoelastic simulation based on facial motion measurement
Hiroki Kato*, Yoshihiro Kuroda*, Chihiro Tanikawa**, Shunsuke Yoshimoto*, Osamu Oshiro*, Kenji Takada**** Graduate School of Engineering Science, Osaka University, Osaka, Japan.
** Graduate School of Dentistry, Osaka University, Osaka, Japan.
*** Faculty of Dentistry, National University of Singapore, Singapore.

Abstract
Facial expression is caused by facial skin deformation. Therefore, one's expression can be affected by the characteristics
of facial skin. Because the characteristic changes are caused by a variety of reasons, such as aging and plastic surgery, it isimportant to consider the effect of characteristics for facial expression. While a lot of facial physical models have been
proposed, the prediction of personal facial expression with facial skin characteristic changes is difficult due to the necessityof the personal facial modeling.
In this research, we propose expression reconstruction using facial shape deformation simulation based on facial skinviscoelasticity and facial motion measurement to consider use's facial skin characteristic changes. The proposed reconstruction method combines user's facial motion data and physical simulation of facial skin deformation. In our method, the facial
motion is determined based on motion measurement without considering mimic muscle model. The user's facial motion ismeasured with a commodity depth sensor. The motion data are used to determine the displacement of a part of the nodes ofthe facial model as driven nodes which are chosen based on mimic muscle alignment and preliminary experiment. Viscoelastic finite element model is used to simulate the facial shape deformation because facial skin is considered as a viscoelastic
material in the proposed method.
In the experiment, 3D facial shape, facial motion, and viscoelastic parameters of a participant were obtained to constructpersonalized facial model. The facial motion was measured with commodity depth sensor and five types of facial motions(instructed smiling, natural smile, lip purse, mouth opening, and cheek puff) were measured. The facial motions were expressed as a time series of displacement of the nodes of 3D facial shape consisting of over 1,000 nodes. We used a part of
the nodes of the time series data to determine facial motion. The viscoelastic parameters of 68 points on the subject’s facewere measured with cutometer. We determined the nodes that are moved based on motion data by comparing the reconstructed result and motion measurement in various conditions.
Keywords: facial expression, feature tracking, viscoelasticity, 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.

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-05 (2017-03)

Structure of soft biological tissues based on viscoelastic propeties (物性から考察する生体軟組織の構造)
Yo Kobayashi* ** **** Department of Mechanical Science & Bioengineering, Graduate school of Engineering Science, Osaka University** Future Robotics Organization , Waseda University*** PRESTO researcher, Strategic Basic Research Pro grams, JST,

Abstract
Understanding the properties of soft biological tissue is a key issue for mechanical systems used in the health care field.
We found from the experiments that soft biological tissues have specific and complex viscoelastic properties and propose
a simple model to identify the viscoelastic properties. I would like to share some of our work in mechanical model of soft
biological tissues and the relationship between properties and structure [1]. The result also suggests that liver tissue has acomplex fractal structure where the liver tissue is mechanically equivalent to a cubed fractal structure (fractal of a fractalof a fractal) whose basic elements are elastic and viscous elements.
Keywords: mechanical properties, viscoelastic properties, fractal structure, scale free
Reference:[1] Yo Kobayashi, Mariko Tsukune, Tomoyuki Miyashita, and Masakatsu G. Fujie, "Simple empirical model for identi
fying rheological properties of soft biological tissues", PHYSICAL REVIEW E, 95, 022418, 2017[2] Mariko Tsukune, Yo Kobayashi, Tomoyuki Miyashita, G. Masakatsu Fujie, "Automated palpation for breast tissue
discrimination based on viscoelastic biomechanical properties", International Journal of Computer Assisted Radiology and Surgery, 10(5), pp 593-601, 2015[3] Yo Kobayashi, Takao Watanabe, Takeshi Ando, Masatoshi Seki, Masakatsu G. Fujie, “Soft Interaction between
Body Weight Support System and Human using Fractional Impedance Control”, Advanced Robotics, Volume 26,
Issue 11-12, pp.1253-1269, 2012[4] Naomi Okamura, Mariko Tsukune, Yo Kobayashi, Masakatsu G. Fujie, "A Study of Viscoelasticity Index for Evaluating Muscle Hypotonicity during Static Stretching", 36th Annual International Conference of the IEEE Engineeringin Medicine and Biology Society(EMBC2014), pp. 6919-6923, 2014[5] Yo Kobayashi, Hiroki Watanabe, Takeharu Hoshi, Kazuya Kawamura, Masakatsu G. Fujie,” Viscoelastic and Nonlinear Liver Modeling for Needle Insertion Simulation”, Soft Tissue Biomechanical Modeling for Computer Assisted
Surgery, Studies in Mechanobiology, Tissue Engineering and Biomaterials Volume 11, pp 41-67, Springer, 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.

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-06 (2017-03)

Development of Feature Extraction Method based on Stacked
Convolutional Neural Networks for Cancer Pathological Images
Naoaki Ono*, Victor Andrew Antonio*, Akira Saito**, Tetsuo Sato***, Md. Altaf-Ul-Amin*, Shigehiko Kanaya**
Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan.
** Division for Health Service Promotion, University of Tokyo, Tokyo, Japan.
*** Graduate School of Radiological Technology, Gunma Prefectural College of Health Sciences, Gumma, Japan.

Abstract
Recent development of computational image recognition based on deep neural network allows us to recognize andclassify wide range of complex images automatically. Convolutional neural networks are one of the promising approaches
for analysis of complex images because of its powerful potential for image analysis without using predefined features. In
this study, we present two applications of the neural networks; a classifier that distinguishes transcriptome subtypes oflung adenocarcinoma from slices of pathological images, and a predictor of pathological images between different staining methods. In order to recognize the types of tumors, we need not only to detail features of cells, but also incorporate
statistical distribution of the different types of cells within the tumor tissue. We constructed a model of deep neural network for feature extraction from pathological images of lung adenocarcinoma, which compromise three gene expression
subtypes previously defined by the Cancer Genome Atlas (TCGA). Since the tumor tissues are composed of heterogeneous cell populations, recognition of tumor transcriptome subtypes requires more information than local pattern of cells but
statistical distribution of cell patterns. Thus we used multiple reduction stages to integrate information from larger area of
pathological images. We demonstrate that this network successfully recognized morphological features of lung adenocarcinoma, and classified them into three transcriptome subtypes with 99.6% accuracy. Moreover, we also applied this model
to predict multi staining pathological images of pancreas cancer. By training two stages of autoencoder and predictor for
immono staining images, which represents cell replication activity were predicted from images of HE staining of the contact slices. Those models will be applied for evaluation and prediction of other types of multiple staining methods.
Keywords: biomedical imaging, machine learning, deep convolutional neural networks, cancer pathological images.
1This article has been printed without reviewing and editing as received from the authors, and the copyright of the article
belongs to the authors.

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 2017/3
JSMBE-MBI2016-07 (2017-03)

Proceedings of Medical and Biological Imaging - JSMBE 2017/3

Clustering Biomedical Information by Deep Learning
Seiichi Tagawa* and Jun Miyake** Graduate School of Engineering Science, Osaka University, Osaka, Japan.

Abstract
Deep learning is a well-known method for feature extraction, classifier, and clustering. We examined if the method
is of use to understand the unknown implicit feature in the given labeled data to biomedical information. In thisresearch, we address two topics: DNA analysis and cell phenotype analysis.
[DNA analysis] DNA sequences has often been categorized using SNP (single nucleotide polymorphism). The SNP
explains the temporal changes of genome sequences. Although we easily understand genome mutations through the
SNPs, there is no proofs that essential features can be enveloped by SNP-based phylogenetic tree. We addressed
letting a stacked auto-encoder learn DNA sequences and confirmed that the reduced dimensions show clusters. The
results indicate the deep learning auto-encoder can provide natural categories.
[Cell phenotype analysis] Small-molecule compounds into cells often bring out cell morphology. In the morphing,
the cell appearance diverges, e.g. enlarged nucleus; increased mitochondria; warped membrane shapes. In drug
discovery, phenotypic screenings have been proposed to discover compounds serving as drugs. Phenotypes are derived from cell morphological image features. In this study we apply deep learning to phenotypic profiling. We tried
to determine whether the cell phenotype changes certain compounds when added to cells by using the Convolutional
Neural Network (CNN), which is a deep learning architecture specified to visual analysis. In experiment, we learned
1854 CNNs for 1854 compounds. In the results, although cell images are not clearly distinguishable by the authors(Fig. 1), the CNN achieved high recognition accuracy for some compounds. The CNN output can be assumed as afeature value representing a cell phenotype and we used the CNN output values for compound clustering.

ControlHigh Accuracy

Low Accuracy

Lowest Accuracy

Figure 1. Fluorescence images of cells (each row shows different organelle)with high, low, and lowest recognition accuracy by CNNs.
Keywords: clustering, deep learning, biomedical imaging, DNA analysis, cell phenotype.
Remark
The research has been done by the collaborative group with Mitsubishi Tanabe Pharma Corporation.
- Osaka University: Shuya Yamamoto, Kanako Sugiyama, Satoshi Asatani, Hirohiko Niioka, and the authors- Mitsubishi Tanabe Pharma Corporation: Ryuta Saitou, Hirokazu Nishimura, Kentaro Matsumoto, and Ken Ohno

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

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-08 (2017-03)

Progress on Deep Learning in Computer Vision and its connection with Medical Image Computing
Yuji Oyamada** Department of Electrical Engineering and Computer Sceience, Tottori University, Tottori, Japan.

Abstract
Deep Learning, deeply layered neural networks, has been focused on in many research fields and has been reported its
succeful result. In this talk, we introduce the progress on deep learning in computer vision and its connection between
computer vision and medical image computing.
Keywords: Medical image processing, Computer Vision, Deep LearningThis article has been printed without reviewing and editing as received from the authors, and the copyright of the article
belongs to the authors.

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-09 (2017-03)

Soft Computing in Medical Image Processing
Syoji Kobashi*,*** Advanced Medical Engineering Research Center, University of Hyogo, Hyogo, Japan.
** Immunology Frontier Research Center, Osaka University, Osaka, Japan.

Abstract
Soft computing approaches include fuzzy logic, neural networks, deep learning, support vector machines, evolutional
computation, probabilistic approaches, and chaos theory. They have been introduced into medical image processing because it is an effective approach to handle uncertainties in acquired image data. This talk introduces a fuzzy object model(FOM) to represent the knowledge of anatomical organ shape, and fuzzy connected image segmentation method using
FOM. To present the performance of the proposed method, it has been applied to neonatal brain segmentation in MR
images.
Keywords: Soft computing, Fuzzy logic, Fuzzy object model, fuzzy connectedness, neonatal brain image segmentation.

Fig 1. Experimental results with LOOCV.

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

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-10 (2017-03)

Medical-Engineering collaboration and Biomedical imaging research
Tatsuhiko Arafune1, Yuki Nakano1, Shinji Chiba2, Takashi Suzuki3, Toshikatsu Washio4, Tomoyuki Yano5
1 School of Science and Engineering, Tokyo Denki University, Saitama, Japan
2 Microsoft Japan co. ltd, Tokyo, Japan
3 Institute of Advanced BioMedical Engineering and Science, Tokyo Women’s Medical University, Tokyo, Japan
4 National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan
5 Plastic Surgery, Gent University, Belgium

Abstract
We will introduce that our approach for medical-engineering collaboration with plastic and reconstructive sutrgeon andimage processing based breast reconstruction plastic surgery assist system on behalf of the M-E collaboration. The breastreconstruction surgery assist system is constructed by 3D capture sensor (Kinect V2, Microsoft co. ltd), image processing
software and projection-mapping system.
Keywords: biomedical imaging, breast reconstruction, plastic surgery, 3D imaging
1

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

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-11 (2017-03)

Evaluation of machine learning classifier for glaucomatous optic
nerve head structure using optical coherence tomography images
Guangzhou An*,**, Kazuko Omodaka***, Satoru Tsuda***, Yukihiro Shiga***, Naoko Takada***, Tsutomu Kikawa*,
Toru Nakazawa***,****, Hideo Yokota**,****, Masahiro Akiba*,***Topcon Corporation, Tokyo, Japan**
Cloud-Based Eye Disease Diagnosis Joint Research Team, RIKEN Center for Advanced Photonics, RIKEN, Japan*** Tohoku University Graduate School of Medicine, Sendai, Japan**** Image Processing Research Team, RIKEN Center for Advanced Photonics, RIKEN, Japan

Abstract
The English abstract is presented here. Minimum length is 50 words; maximum length is 350 words.
Glaucoma is a multifactorial eye disease resulting in damage to optic nerve. It is supposed that Nicolela’s classificationof optic nerve head based on its structure, including four types, seems to be useful to know glaucomatous factors, and selectproper treatments for patients with open-angle glaucoma (OAG). However, it depends entirely on subjective judgments
acquired from color fundus photographs. This study aimed to develop a machine learning classification model to identify
optic nerve head structure objectively. This study enrolled 113 eyes of 113 OAG patients. Fifty five parameters were applied
in total, including the demographic data, and a variety of quantified ocular parameters from swept-source optical coherence
tomography images: circumpapillary retinal nerve fiber layer thickness (cpRNFLT) and optic nerve head structure. Data
was reviewed and graded by three glaucoma specialists. From all the eyes, 76 eyes were randomly selected as training groupdata, to build a classification model, while the remaining 37 eyes were used as test group data to validate the performanceof the built model. Three machine learning classifiers were used and compared the performances: Neural Network (NN),Support Vector Machine (SVM) and Naïve Bayes (NB). To employ the most effective features in machine learning, ahybrid feature selection method combining mRMR (minimum Redundancy Maximum Relevance) and feature
selection method based on genetic algorithm. As a result, accuracy of the classification for NN, SVM, NB were 81%,79%, and 77%, respectively. All quantified ocular parameters were ranked by their importance, calculated by the bestmodel (NN). The 7 most important features were disc angle (horizontal), average cup depth, spherical equivalent, horizontal
Cup/Disc Ratio, Disc Height Difference, age, and average superior nasal clock-hour cpRNFLT. The proposed method makes
it possible to objectively classify the glaucomatous optic nerve head based on its morphology with good accuracy, without
using color fundus photos. Furthermore, the confidence score of predicting optic nerve head shape, calculated by the classification model, was supposed to be valid, in the case of having multiple factors simultaneously.
Keywords: machine learning, open-angle glaucoma, optical coherence tomography, optic nerve head.

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

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-12 (2017-03)

Partial region learning in CNN and majority voting for breast
lesion classification in mammograms
Kaihei Kitamura*, Ryo Takeuchi**, Masahito Aoyama*, Hisashi Fujiwara*, Yoshitaka Masutani** Department of Biomedical Information Sciences, Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan** Department of Biomedical Information Sciences, Faculty of Information Sciences, Hiroshima City University, Hiroshima, Japan

Abstract
In recent years, breast cancer has been increasing in Japan. Therefore, the importance of early detection, early diagnosis
and early treatment of cancer has increased, and the importance of mass screening is being recognized. However, for physicians engaged in diagnosis, an increase in interpretation work in mass screening is a considerable burden. Therefore, it isnecessary to develop a computer-aided diagnosis (CAD) system in which a doctor uses the result quantitatively analyzed
by a computer as a "second opinion".
In this study, learning and evaluation experiments were carried out with the aim of achieving benign-malignant discrimination of mammograms using computer with deep learning. As a method of deep learning, a convolution neural network(CNN) which is learning method targeting general image recognition was used. In fact, machine learning requires a large
amount of data, but it is difficult to obtain a large amount of data due to problems such as protection of personal information. Therefore, we extracted multiple small regions from one image, prepared a large amount of learning data withdiversity, and carried out partial region learning. In our method, first, small regions are extracted from a single region ofinterest (ROI) of lesion with randomized locations and sizes. Then, the small regions are scaled to fit the size of the CNN
input. Finally, based on multiple CNN outputs for the small regions, final decision of classification for the lesion is madeby majority voting. In our preliminary studies, approximately 84% of correct classification rate was achieved by using
11-fold cross validation with the open database of MIAS and DDSM.
In summary, our method based on CNN with partial region learning and majority voting is feasible for breast cancer
classification.
Keywords: breast lesion classification, deep learning, partial region learning, majority voting

Figure 1. Examples of extracting small areas (8 pieces), performing differential diagnosis by majority votingThis article has been printed without reviewing and editing as received from the authors, and the copyright of the article
belongs to the authors.

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-13 (2017-03)

Multi-class diagnosis for Parkinsonism with support vector machine using DaTSCAN SPECT images
Shogo Watanabe*, Yuichi Kimura*, Toshiyuki Hayashi**, Masahiro Mishina**** Graduate School of Biology-Oriented Science and Technology, Kindai Univeristy, Japan.
** Department of Neurological Science, Graduate School of Medicine, Nippon Medical School*** Department of Neuro-pathophysiological imaging, Graduate School of Medicine, Nippon Medical School.

Abstract
Parkinson disease (PD) is a progressive neurological disorder and is included Parkinson syndrome (PS). PS contains
some diseases: PD, dementia with Lewy body (DLB), progressive supranuclear palsy (PSP), and Alzheimer disease (AD).
In clinical diagnosis, DaTSCAN SPECT imaging is conducted. The binary classification between normal (NR) and PD has
been investigated [1][2], but multi-class classification for PS has not been reported. The aim of our research is a multi classclassification for PS based on DaTSCAN SPECT images and other information by using machine learning. Our proposed
method located a rectangle ROI around the striatal and normalized by whole brain mean count. Next, we trained all voxel
values and diagnosis label by a classification algorithm, and evaluated the performance. In our previous research, we conducted the binary classification NR and PD. We trained image data (213 NR cases and 1189 PD cases) from Parkinson
Progression Marker Initiative (PPMI) database by support vector machine (SVM). On this study, we evaluated learned SVM
using clinical data (7 NR cases and 15 PD cases) provided from Nippon Medical School. In a result, SVM derived theaccuracy of 86.4%. This performance against clinical data is effective. Therefore, we try to classify PS on the next phase.
Moreover, we must consider the method to combine image data and other information, because, for instance, age is closely
related to PS.
[1] Francisco P. M. Oliveira et al. , 2015, “Computer-aided diagnosis of Parkinson’s disease based on 123IFP-CIT SPECT
binding potential images, using the voxel-as-features approach and support vector machines”, J. Neur. Engi., 12[2] Shogo Watanabe et al., 2017, “Investigation of Potential Usability of Machine Learning to Diagnosis of Parkinson
Disease Using SPECT DaTSCAN”, IFMIA2017
Keywords: DaTSCAN SPECT, Parkinson syndrome, machine learning.

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

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-14 (2017-03)

Stability analysis of a dynamical system describing competition for
multi-valued discrete tomography
Takeshi Kojima*, Tetsushi Ueta**, and Tetsuya Yoshinaga**** Graduate School of Advanced Technology and Science, Tokushima University, Tokushima, Japan.
** Graduate School of Science and Technology, Tokushima University, Tokushima, Japan.
*** Institute of Biomedical Sciences, Tokushima University, Tokushima, Japan.

Abstract
We previously proposed a method for multi-valued discrete tomography using a dynamical system enabling an exclusive
behavior of a competitive model. The dynamical system has been designed so that the solutions starting from appropriate
initial values converge to the true solution corresponding to the exact image.
We have proved the coexistence of the true solution and the false solutions as stable equilibria and the existence of thesaddle type equilibrium that plays an important role in the behavior of solutions. Figure 1 shows an example of the phase
portraits obtained by projecting the trajectories in the eight-dimensional phase space into the three-dimensional phase space.
The trajectories emanating from different initial values go towards the saddle 𝑧 along the stable manifold, and then after
approaching to 𝑧, they leave from 𝑧 and converge to either the true solution or the false solution depending on their initialvalue. The results suggest that the stable manifold of 𝑧 separates into the basin which converges to the true solution.
Keywords: multi-valued discrete tomography, image reconstruction, nonlinear differential equation, competitive model

True solution

True solution

𝑥/

𝑥/
𝑧

𝑧
False solution

False solution

𝑥'

𝑥%

(a)

𝑥'

Figure 1: Trajectories of solutions

𝑥%

(b)

𝑥 = (𝑥% , 𝑥' , … , 𝑥) ) denote state variables. Blue lines represent the trajectories emanating from uniformed initial value (a) 0.7 and(b) 0.8. Closed circle represents stable equilibrium. Open circle represents saddle type equilibrium 𝑧. Green dotted line represents
stable manifold of 𝑧. Red dotted line represents unstable manifold of 𝑧.

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

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-15 (2017-03)

Kinematic analysis of forearm rotation by 2D-3D registration
using biplane fluoroscopy
Yusuke Tenma*, Yuta Hiasa*, Yoshito Otake*, Shingo Abe**, Hirokazu Kato*, 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 orthopedics, quantification of three-dimensional kinematics of the bones is useful for diagnosis and treatment planning. In case of forearm, 3D motion analysis of pronation and supination is important to clarify the anatomical function.
Shian et al [1] focused on the motion of the radius around the ulna and analyzed the movement of 3D rotation axis between maximum pronation and maximum supination using four CT images in different postures. Kazama et al [2] usedbiplanar radiographies in five postures and a CT image. In order to understand the anatomical function in more detail in aclinical routine, a method which allows 3D motion analysis with high temporal resolution while keeping the radiationdose at the conventional level is essential. Our goal is to analyze the detailed mechanism of dislocation including a quick
movement. For this purpose, we propose a 2D-3D registration method using a calibrated biplanar dynamic fluoroscopy
and a CT image. This method follows that of Otake et al [3] in which simultaneously optimize the rigid transformation
parameters of multiple bones by maximizing the similarity between the fluoroscopy and digitally reconstructed radiograph (DRR). We use CMA-ES as optimizer and gradient correlation similarity metric.
In this paper, we report a simulation experiment using a bone phantom and a real image experiment using clinical dynamic fluoroscopy(12.5 fps, 125 frame images). In the simulation
experiment, we compared the effects of accuracy
on different radiation dose levels. The registration
(a)
(b)
(c)
(d)
accuracy was evaluated using Euclidean distanceFig.1 Example 3D kinematics from the clinical fluoroscopy images. (a) LAT
error between the marker position on the fluoroscopy and estimated marker position on DRR. The image, (b) AP image, (c) and (d) LAT and AP images overlay estimated 3D
bone position.
average error at low, middle and high radiationdose mode were 1.96 mm, 2.05 mm and 1.72mm, respectively. Fig.1 shows 3D kinematics estimated from the clinical fluoroscopy images. Fig.
2 shows the angle of rotation from the neutral position. Our future work includes introducing
smoothness constraint to improve robustness andaccuracy in 2D-3D registration.
Fig.2 The angle of rotation from the neutral position.

Keywords: kinematic analysis, forearm, 2d3d registration

References[1] Shian Chao Tay，Roger van Riet, et al. "In-vivo kinematic analysis of forearm rotation using helical axis analysis." Clinical Biomechanics 25 (2010) 655-659.
[2] 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 .
[3] Otake, Y, Armand, M., Armiger, R.S, et al. “Intraoperative image-based multiview 2d/3d registration for imageguided orthopaedic surgery: incorporation of fiducial-based c-arm tracking and gpu-acceleration” IEEE transactions
on medical imaging 31 (2012) 948–962

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

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 2017/3
Proceedings of Medical and Biological Imaging - JSMBE 2017/3

JSMBE-MBI2016-16 (2017-03)

Database construction of pre- and post-operative musculoskeletal
structures and implants towards statistical modeling in total hip
arthroplasty
Kazuma Suenaga*,Koki Koyama*,Yuki Nakanishi*, Futoshi Yokota*,Yoshito Otake*, Masaki Takao**, Takeshi Ogawa** ,Keisuke Uemura** , Nobuhiko Sugano **, Yoshinobu Sato** Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan** Department of Orthopaedic Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan.

Abstract
3D surgical planning systems for total hip arthroplasty (THA) require a complicated user interaction considering thebalance of various clinical evaluation criteria, and requires a lot of labor, time and skill.
We have been developing an automated surgical planning system for THA based on statistical analysis of dataset of pastpatients. This system produce a surgical plan automatically from the patient CT image. Although a planning considering
postoperative implant position and soft-tissue tension is desirable [1], the database of our system currently consists of only
preoperative data.
The goal of this study is to create a surgical plan for THA in consideration of CT images before and after surgery. In thispaper, we report on the database we created. The database includes (1) the relative displacement of the bones (i.e., pelvis
and femur) before and after surgery, (2) the muscle shape deformation between preoperative and postoperative CT, (3) the
difference of implant position that is planned preoperatively and the actually placed location. The method to determine
each element is described below.
(1) Relative displacement of the bones. (1-1) The pelvis and femur were segmented manually from CT images, (1-2)
registration was performed on the pelvis before and after surgery, (1-3) the relative displacement was obtained from theregistration results.
(2) Muscle shape deformation. (2-1) The gluteus medius muscle was manually segmented from CT image, (2-2) thetransformation matrix obtained in (1-2) was applied to align the coordinate system, (2-3) pseudo x-ray images of the preand postoperative gluteus medius muscle was created.
(3) Difference of implant position. (3-1) The transformation matrix of the implant in preoperative plan was obtained fromthe surgical plan, (3-2) 3D-3D registration between the postoperative CT and the CAD model of the implant was performed
to find the postoperative implant position.
Future work includes creating a surgical plan using this database.

Keywords: Total hip arthroplasty, Database, Automatic surgical plan
Reference[1] M.Takao, T.Nishii, T.Sakai, N.Sugano, “Postoperative Limb-Offset Discrepancy Notably Affects Soft-Tissue Tension
in Total Hip Arthroplasty” The Journal of bone and joint surgery. American volume. 98(18),pp.1548-54, Sep 2016.

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