The 1st Conference on Biological Imaging and Medical AI
Program & Abstract
March 16, 2019
Multi-purpose Hall, Gunma Prefectural College ofHealth Sciences  $+%')1, - ./2019316
*0#(#& "! 

- CONTENTSJSMBE-BIMAI2018-01

MR Compressed Sensing Reconstruction using Image Domain Learning CNN
Shohei Ouchi, Satoshi Ito (Utsunomiya University)
JSMBE-BIMAI2018-02

Study on initial alignment on registration between clinical CT and micro CT volumes oflungs
Shinya Hatakoshi, Hirohisa Oda, Takaaki Sugino, Xiaotian Zhao, Takayasu Moriya, Holger R. Roth,Shota Nakamura, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori (Nagoya University)
JSMBE-BIMAI2018-03[Invited Lecture] Role of image processing and machine learning in Carbon-ion
radiotherapy
Yoshiki Kubota (Gunma University)
JSMBE-BIMAI2018-04

Continuous observation of nailfold capillaroscopy through Image Processing
Hieyong Jeong, Yuko Ohno (Osaka University)
JSMBE-BIMAI2018-05

Composite visco-elastic model for replaying facial expression
Yoshihiro Kuroda, Hiroki Kato, Chihiro Tanikawa, Shunsuke Yoshimoto,Osamu Oshiro (Osaka University), Kenji Takada (National University of Singapore)
JSMBE-BIMAI2018-06

Endoscopic Image Sequence Clustering using Dynamic Programming
Shota Harada, Hideaki Hayashi, Ryoma Bise (Kyushu University),
Kiyohito Tanaka (Kyoto Second Red Cross Hospital),
Qier Meng (Research Center for Medical Bigdata, National Institute of Informatics),
Seiichi Uchida (Kyushu University)

̶目次̶
JSMBE-BIMAI2018-01

CNN を利⽤した Image Domain Learning による MR 圧縮センシング再構成
⼤内 翔平, 伊藤 聡志（宇都宮⼤学）
JSMBE-BIMAI2018-02

肺の医療⽤ CT 像とマイクロ CT 画像のレジストレーションにおける初期位置合
わせの検討
波多腰 慎⽮, ⼩⽥ 紘久, 杉野 貴明, 趙 笑添, 守⾕ 享泰, Holger R. Roth, 中村 彰太,林 雄⼀郎, ⼩⽥ 昌宏, 森 健策（名古屋⼤学）
JSMBE-BIMAI2018-03[招待講演] 炭素線治療における画像処理と機械学習の役割
久保⽥ 佳樹（群⾺⼤学）
JSMBE-BIMAI2018-04

画像処理による指先⽑細⾎管の継続的観察⽅法
丁 憙勇, ⼤野 ゆう⼦（⼤阪⼤学）
JSMBE-BIMAI2018-05

表情再⽣のための複合粘弾性モデル
⿊⽥ 嘉宏, 加藤 弘樹, ⾕川 千尋, 吉元 俊輔, ⼤城 理（⼤阪⼤学）,
⾼⽥ 健治（シンガポール国⽴⼤学）
JSMBE-BIMAI2018-06

動的計画法を⽤いた内視鏡画像系列クラスタリング
原⽥ 翔太, 早志 英朗, 備瀬 ⻯⾺（九州⼤学）, ⽥中 聖⼈（京都第⼆⾚⼗字病院）,
Qier Meng（国⽴情報学研究所医療ビッグデータ研究センタ）, 内⽥ 誠⼀（九州⼤学）

日本生体医工学会 専門別研究会 生体画像と医用人工知能研究会
若手発表会 2019/3
Proceedings of Biological Imaging and Medical AI - JSMBE 2019/3JSMBE-BIMAI2018-01 (2019-03)

MR Compressed Sensing Reconstruction using Image Domain
Learning CNN
Shohei Ouchi*, Satoshi Ito*
* Graduate School of Engineering Information and Systems Science, Utsunomiya University, Utsunomiya, Japan.

Abstract
Recently, a new theory called compressed sensing (CS) has been applied to MR image reconstruction with great success.
CS reconstruction requires an iterative process for image reconstruction, which is computationally far more intensive than traditional inverse Fourier reconstruction. Another drawback of CS is that the obtained image tend to have artificial appearances.
In this study, we propose a high-speed CS reconstruction method using Image Domain deep learning. Our networks is basedon residual learning convolutional neural network with 30 layers [1]. The reconstructed image is obtained by subtracting aliasing artifacts on the zero-filled images obtained by under-sampled MR signal. Reconstruction experiments showed that proposed method outperforms existing iterative reconstruction method (RecPF [2], sparseMRI [3]) in terms of reconstruction timeand image quality.

Keywords: compressed sensing, reconstruction, deep learningReferences[1] Zhang K, Zuo W, Chen Y et al. “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising”
IEEE Transactions on Image Processing, 26, pp.3142-3155, 2017.

[2] Yang J, Zhang Y, Yin W “A Fast Alternating Direction Method for TVL1-L2 Signal Reconstruction From Partial
Fourier Data” IEEE Journal of Selected Topics in Signal Processing, 4, pp.288-297, 2010.
[3] Lustig M, Donoho DL and Pauly JM “Sparse MRI: The application of compressed sensing for rapid MR imaging”
Magnetic Resonance in Medicine, 58, pp.1182-1195, 2007.

Fig. 1 Reconstructed images

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 生体画像と医用人工知能研究会
若手発表会 2019/3
Proceedings of Biological Imaging and Medical AI - JSMBE 2019/3

JSMBE-BIMAI2018-02 (2019-03)

Study on initial alignment on registration between clinical CT andmicro CT volumes of lungs
Shinya Hatakoshi*, Hirohisa Oda*, Takaaki Sugino*, Xiaotian Zhao*, Takayasu Moriya*, Holger R. Roth*,Yuichiro Hayashi*, Shota Nakamura**, Masahiro Oda*, Kensaku Mori*,***,***** Graduate School of Informatics, Nagoya University, Nagoya, Japan.
** Nagoya University Graduate School of Medicine, Nagoya, Japan.
*** Information Technology Center, Nagoya University, Nagoya, Japan**** Research Center for Medical Bigdata, National Institute of Informatics, Tokyo, Japan

Abstract
This paper presents initial alignment on registration between clinical CT and micro-focus X-ray CT (micro CT) volumesof lung. Clinical CT and MRI are 3D imaging modalities of mm-scale level resolution, and are commonly clinically used.
Micro CT is a modality which enables us to observe μm-scale level of resolution images. While the micro CT cannot be
used for the human body, resected specimens of lung tissues can be scanned. Registration of clinical and micro CT volumes
allows us to explore macroscopic and microscopic structures. Our goal is to perform registration of pre-operative clinical
CT of lung patients and micro CT volumes of those patients’ resected lung cancers. Nevertheless several multi-modality
registration methods have been tried, registration of those clinical and micro CT volumes have not been succeeded. In thisstudy, we propose an initial rigid registration method between clinical CT and micro CT volumes of lung which can be
performed as initial alignment of our future non-rigid registration method. Firstly, rough structures using information of
surface and cutting plane of both CT volumes are aligned. Then, we segment blood vessels from both volumes, and convert
the blood vessel labels into point clouds. Registration consists of two steps: 1) Rough alignment using manually selectedpairs of landmark points in the point clouds, and 2) detailed alignment using ICP on the point clouds of the blood vessels.
Finally, we apply the transformation matrix obtained by this method to the micro CT volume. Experiments were performed
using one pair of clinical and micro CT volumes of a lung cancer patient. It was confirmed that not only blood vessels used
for alignment but also structures outside them were also aligned almost properly. Quantitatively, it was confirmed that thevalue of NCC is closer to 1 after ICP.
Keywords: microstructural computational anatomy, scale-seamless registration, 3D histopathological diagnosis, multiresolution images

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 生体画像と医用人工知能研究会
若手発表会 2019/3
Proceedings of Biological Imaging and Medical AI - JSMBE 2019/3

JSMBE-BIMAI2018-03 (2019-03)

Role of image processing and machine learning in Carbon-ion
radiotherapy
炭素線治療における画像処理と機械学習の役割
Yoshiki Kubota
Gunma University Heavy Ion Medical Center, Maebashi, Japan.

Abstract
Carbon-ion radiotherapy is one of main treatment modalities for cancers. It has higher dose conformity and biology
effect than photon therapy. Therefore, this technology has high requirement for accuracy. To achieve a more accurate irradiation, various technologies including the physical and engineering sites are applied, especially for image processing whichplays an important role. Moreover, artificial intelligence including machine learning is being applied recently. I will present
the role of the image processing and machine learning for carbon-ion radiotherapy and their future prospect.
Keywords: Carbon-ion radiotherapy, image processing, 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 生体画像と医用人工知能研究会
若手発表会 2019/3
Proceedings of Biological Imaging and Medical AI - JSMBE 2019/3

JSMBE-BIMAI2018-04 (2019-03)

Continuous observation of nailfold capillaroscopy through image processing
Hieyong Jeong*, Yuko Ohno*** Department of Robotics & Design for Innovative Health Science, Graduate School of Medicine, Osaka University,Japan.
** Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Japan.

Abstract
Routine nailfold capillaroscopy is essential for early diagnosis of rheumatic disease, but clinicians struggle to evaluate
the same capillaries in every examination because the capillaries are similar in shape; therefore, difficult to differentiate.
Here, we propose a method that enables us to compare previous and real-time images captured by a microscope with lowand high-power objective lenses. After selecting a target nailfold capillary using the low-power objective lens, we assessed
the capillary with a high-power objective lens. The captured image of the target capillary was compared in real-time withthe previously captured image, irrespective of magnification, using template matching. Our proposed algorithm detected
allowable similarity for assessing the same capillary, which was helpful in evaluating the structural abnormalities of theentire area in addition to just the magnified area.
Keywords: Image stabilization, Nailfold capillary, Microscopy with low- and high-power objective lenses, Real-time similarity check.

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 生体画像と医用人工知能研究会
若手発表会 2019/3Proceedings of Biological Imaging and Medical AI - JSMBE 2019/3

JSMBE-BIMAI2018-05 (2019-03)

Composite Visco-elastic Model for Replaying Facial Expression
Yoshihiro Kuroda*, Hiroki Kato*, Chihiro Tanikawa**,***, Shunsuke Yoshimoto*, Osamu Oshiro*, Kenji Takada**,****
*
**
***
****

Graduate School of Engineering Science, Osaka University, Toyonaka, Japan.
Global Center for Medical Engineering and Informatics, Osaka University, Suita, Japan.
Graduate School of Dentistry, Osaka University, Suita, Japan.
Faculty of Dentistry, National University of Singapore, Singapore.

Abstract
Facial expression has an important role in communication of human being. Aging and surgical scar change physicalcharacteristics of facial tissue so that the difference of facial movement influences impression of the person. Recently, thecomputer-based biomechanical simulation provides an estimated result of aging and surgical scar, although most studies arelimited to static movement. The goal of this study is to simulate dynamic movement of facial tissue considering viscoelastic characteristics to explain the effect of the procedure on dynamic movement. In this presentation, we report the measured visco-elastic properties of a couple of points of face and the results of dynamic deformation by using finite elementmethod.
Keywords: finite element method, visco-elasticity, facial expression, deformation.

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 生体画像と医用人工知能研究会
若手発表会 2019/3
Proceedings of Biological Imaging and Medical AI - JSMBE 2019/3

JSMBE-BIMAI2018-06 (2019-03)

Endoscopic Image Sequence Clustering using Dynamic Programming
Shota Harada*, Hideaki Hayashi**, Ryoma Bise**, ***, Kiyohito Tanaka****, Qier Meng***, Seiichi Uchida**, **** Graduate School of Systems Life Sciences Kyushu University, Fukuoka, Japan** Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan*** Research Center for Medical Bigdata, National Institute of Informatics, Japan**** Department of Gastroenterology, Kyoto Second Red Cross Hospital, Japan}

Abstract
Endoscopic image is an important modality for the discovery of diseases. Nowadays, it is natural to use some machinelearning techniques such as neural networks and support vector machines for supporting endoscopic diagnosis by computers. However, the collection of labeled endoscopic images for a training dataset is difficult. In such case, group-basedlabeling is an effective strategy for collecting sufficient amount of training data. Improving the performance of endoscopic
image clustering accelerates such group-based labeling.
In this study, we propose a new unsupervised learning method for clustering endoscopic images. A common property ofendoscopes is that they take a sequence of images according to their camera movement. Hence, we use the temporalordering information to cluster the endoscopic images. In the proposed method, clustering results of endoscopic image
sequences are obtained by minimizing variance in each cluster using dynamic programming. Clustering is performed oneach endoscopic image sequence.
In the experiments using esophagogastroduodenoscopy image datasets, we showed the validity of the proposed method
for clustering endoscopic image sequence. The proposed method showed the best performance compared with conventional clustering method.
Keywords: endoscopic image, clustering, dynamic programming

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