The 2nd Conference on Biological Imaging and Medical AI
Program & Abstract
March 10, 2020
Saitama Hatoyama Campus, Tokyo Denki University

生体画像と医用人工知能研究会 第2回若手発表会
プログラム・抄録集
2020年3月10日
東京電機大学 埼玉鳩山キャンパス

- CONTENTSJSMBE-BIMAI2019-01

Classification of pick and grasp manipulation based on fingertip grip force and velocity
Tatsuya Yamashita, Megumi Nakao, Tetsuya Matsuda (Kyoto University)
JSMBE-BIMAI2019-02

A preliminary study for improving image quality of cone-beam CT images using 3D
generative adversarial network
Takumi Hase, Megumi Nakao (Kyoto University), Keiho Imanishi (e-Growth Co., Ltd.),
Nakamura Mitsuhiro, Tetsuya Matsuda (Kyoto University)
JSMBE-BIMAI2019-03

Development of support system during breast reconstruction using real-time
three-dimensional information processing
Kotaro Kaneko (Tokyo Denki University), Takashi Suzuki (Tokyo Women’s Medical University),
Shinji Chiba (Microsoft Japan),Toshikatsu Washio (National Institute of Advanced Industrial Science and Technology),
Tomoyuki Yano (Cancer Institute Hospital), Tatsuhiko Arafune (Tokyo Denki University)
JSMBE-BIMAI2019-04[Invited Lecture] Hearing in the right ear: A neurophysiological approach
Keita Tanaka (Tokyo Denki University)
JSMBE-BIMAI2019-05

Preliminary study on segmentation of cervical intervertebral disks in videofluorography
by multi channelization with various image filters and CNN
Ayano Fujinaka (University of Tsukuba), Kojiro Mekata (Kobe Red Cross Hospital),
Hotaka Takizawa, Hiroyuki Kudo (University of Tsukuba)
JSMBE-BIMAI2019-06

A feasibility study on predicting synthetic dual-energy X-ray fluoroscopic images using deep
convolutional neural network
Jiaoyang Wang, Kei Ichiji, Noriyasu Homma (Tohoku University)
JSMBE-BIMAI2019-07

Fully automatic vertebra segmentation for spine and pelvis alignment analysis in a large-scale
CT database
Zhuo Cheng, Yuki Tanaka, Yoshito Otake (Nara Institute of Science and Technology),
Masaki Takao (Osaka University), Keisuke Uemura (Nara Institute of Science and Technology),
Nobuhiko Sugano (Osaka University), Yoshinobu Sato (Nara Institute of Science and Technology)̶目次̶
JSMBE-BIMAI2019-01

指先の把持⼒と速度に着⽬したつまみ及びつかみ操作の分類
⼭下 達也, 中尾 恵, 松⽥ 哲也（京都⼤学）
JSMBE-BIMAI2019-02

3 次元敵対的⽣成ネットワークを⽤いた Cone-Beam CT 画像の画質改善の試み
⽻瀬 拓視, 中尾 恵（京都⼤学）, 今⻄ 勁峰（イーグロース株式会社）, 中村 光宏,
松⽥ 哲也（京都⼤学）
JSMBE-BIMAI2019-03

リアルタイム三次元情報処理を⽤いた乳房再建術中⽀援システムの開発
⾦⼦ 晃太郎（東京電機⼤学）, 鈴⽊ 孝司（東京⼥⼦医科⼤学先端⽣命医科学研究所）,
千葉 慎⼆（⽇本マイクロソフト株式会社）, 鷲尾 利克（産業技術総合研究所）,
⽮野 智之（がん研有明病院）, 荒船 ⿓彦（東京電機⼤学）
JSMBE-BIMAI2019-04[招待講演] ヒトは右⽿で聞いている．神経⽣理学的アプローチ
⽥中 慶太（東京電機⼤学）
JSMBE-BIMAI2019-05

フィルタを増やしたマルチチャンネル化と CNN を⽤いた嚥下時 X 線透視動画からの頸椎椎間板抽出の基礎的検討
藤中 彩乃（筑波⼤学）, ⽬⽚ 幸⼆郎（神⼾⾚⼗字病院）, 滝沢 穂⾼, ⼯藤 博幸（筑波⼤学）
JSMBE-BIMAI2019-06

深層畳み込みニューラルネットワークを⽤いた Dual Energy X 線透視像予測の試み王 驕洋, 市地 慶, 本間 経康（東北⼤学）
JSMBE-BIMAI2019-07

⼤規模 CT データベースでの脊柱と⾻盤アラインメント解析のための全⾃動脊椎セグメンテーション
程 卓, ⽥中 雄基, ⼤⽵ 義⼈（奈良先端科学技術⼤学院⼤学）, ⾼尾 正樹（⼤阪⼤学）,
上村 圭亮（奈良先端科学技術⼤学院⼤学）, 菅野 伸彦（⼤阪⼤学）,
佐藤 嘉伸（奈良先端科学技術⼤学院⼤学）

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 2020/3
Proceedings of Biological Imaging and Medical AI - JSMBE 2020/3

JSMBE-BIMAI2019-01 (2020-03)

Classification of pick and grasp manipulation based on fingertip
grip force and velocity
Tatsuya Yamashita*, Megumi Nakao*, Tetsuya Matsuda**Graduate School of Informatics, Kyoto University, Kyoto, Japan.

Abstract
Finger manipulation plays an important role in daily life, and the importance of its analysis is increasing in a variety of
research fields. However, due to the difficulty of measurement, it is difficult to understand the essentials of fingertip manipulations, such as the differences and functions of each manipulation. Although many previous studies have examined
finger manipulations, quantitative differences between finger manipulations are not clarified, or time-series information of
measurement values is lost by converting time series data to one-dimensional features such as their average values. Few
studies have analyzed finger manipulations data in time series. The current study focused on pick and grasp manipulations,
exploring dynamic features of the thumb, index finger, and middle finger. In the past, we conducted research on pick andgrasp manipulations [1], but the time-series information of measurement values was lost by converting time series data to
one-dimensional features. Although the result showed that two manipulations were identifiable, it is not obvious whether
the method of converting time series data to one-dimensional features is optimal. Therefore, the analysis method that
calculates one-dimensional features for one manipulation is extended to the analysis method that calculates N-dimensionalfeatures for one manipulation, and we analyzed the pick and grasp manipulations based on N-dimensional features. The 6channel measurement data on the contact force and velocity of the thumb, index finger, and middle finger are divided into
N sections, and the feature value is calculated in each section to obtain a 6 × N dimensional feature vector. We conducteduser experiments with nine subjects while they manipulated Poly-Lactic-Acid objects. We discriminated pick and grasp
manipulations based on time-series and multi-dimensional features and clarified the number of effective divisions N = 6
and the combination of effective channels for the discrimination of two manipulations.
Keywords: Feature analysis，Fingertip manipulation，Haptics.
Reference[1] Tatsuya Yamashita, Megumi Nakao, Tetsuya Matsuda, Analysis of Fingertip-based Dynamic Features during Pick andHold Manipulation, Transactions of Japanese Society for Medical and Biological Engineering, Volume 57 Issue 2-3 Pages
68-74, 2019. https://doi.org/10.11239/jsmbe.57.68

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 2020/3
Proceedings of Biological Imaging and Medical AI - JSMBE 2020/3

JSMBE-BIMAI2019-02 (2020-03)

A preliminary study for improving image quality of cone-beam CT
images using 3D generative adversarial network
Takumi Hase*, Megumi Nakao**, Keiho Imanishi***, Mitsuhiro Nakamura****, Tetsuya Matsuda***

Undergraduates School of Electrical and Electronic Engineering, Faculty of Engineering, Kyoto University, Kyoto,
Japan.
** Graduate School of informatics, Kyoto University, Kyoto, Japan.
*** e-Growth Co., Ltd., Kyoto, Japan.
****Graduate School of Medicine and Faculty of Medicine, Kyoto University, Kyoto, Japan.

Abstract
Cone-Beam Computed Tomography (CBCT) images have been widely used in medical field, such as radiation therapy,
surgery diagnosis. However, CBCT images contain inaccurate CT values because of artifacts and missing pixel information.
Although deep learning has achieved great success in image-to-image translation, it is difficult to obtain the paired CT and
CBCT images from the sole patient for supervised training. This difficulty is solved by unsupervised learning, such as
Generative Adversarial Network (GAN) and CycleGAN. Especially CycleGAN has been proposed for the purpose of image-to-image translation based on unsupervised training and does not require the paired CT and CBCT images. However,
CycleGAN may deform anatomical structures or create new ones, even though it effectively removes artifacts and corrects
CT values.
This study aims to improve the image quality of CBCT images in the abdominal region based on unsupervised training.
We propose a method to translate CT values to correct values while keeping the anatomical accuracy by adversarial training
considering the three-dimensional features of CBCT images. Experiments using test data have confirmed that the proposed
method is effective for improving the image quality of CBCT images.
Keywords: unsupervised learning, CBCT, Generative adversarial network, artifact reduction.

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 2020/3
Proceedings of Biological Imaging and Medical AI - JSMBE 2020/3

JSMBE-BIMAI2019-03 (2020-03)

Development of support system during breast reconstruction using real-time three-dimensional information processing
Kotaro Kaneko*, Takashi Suzuki**, Shinji Chiba***, Toshikatsu Washio****, Tomoyuki Yano*****, Tatsuhiko Arafune*

*
**
***
****
*****

School of Engineering Science, Tokyo Denki University, Saitama, Japan.
Institute of Advanced Biomedical Engineering and science, Tokyo Women’s University, Tokyo, Japan.
Microsoft Japan, Tokyo, Japan.
National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan.
The Cancer Institute Hospital of JFCR, Tokyo, Japan.

Abstract
Reduction of the moldability of the postoperative breast shape during breast reconstruction has been cited as an issue.
We have developed a system that obtains three-dimensional shape difference information before mastectomy and duringreconstruction, and maps the local shape difference information to the patient's skin superficial. The system evaluation bya plastic surgeon using this system revealed that it was desirable to be able to grasp shape information more quickly. Therefore, the purpose of this study was using KinectV2 to develop a system that calculates the shape difference information inreal time based on the left and right breast shapes. As a system evaluation, the shape difference during reconstruction was
reproduced using a mannequin simulating a patient, and measurement and projection experiments were performed. It is now
possible to project the shape difference information onto the skin surface without the process of pasting the shape difference
texture onto the three-dimensional shape data.
Keywords: Breast reconstruction, KinectV2, Real-time information projection.

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 2020/3
Proceedings of Biological Imaging and Medical AI - JSMBE 2020/3

JSMBE-BIMAI2019-04 (2020-03)

Hearing in the right ear: A neurophysiological approach
Keita Tanaka** School of Science and Engineering, Tokyo Denki University, Saitama, Japan.

Abstract
A dichotic listening test is presenting two different auditory stimuli simultaneously to the patient, one to each ear. Whenused with speech stimuli, the typical finding for dichotic listening is the so-called right-ear advantage which means that
more stimuli are correctly reported from the right ear. Right ear-advantage is commonly explained as the left temporal cortex
being dominant for perception of elementary speech units and that the dichotic mode of presentation gates the contralateral
input from the ear to the auditory cortex. On the other hand, so far the relationship between right-ear advantage and neurophysiological auditory processing is not clear. Therefore, we used a frequency tagging approach to explore auditory neuralprocessing during dichotic listening. To get such information, we labeled the input at each ear by tagging the speech stimuli
with amplitude modulation of different frequencies at each ear and by following cortical responses at the modulation frequencies.
The magnetoencephalogram was recorded while participants listened to a pair of stimulus sounds, chosen from 48 kindsof Japanese two syllable sounds ("A-ka", "I-nu", etc.), which were amplitude-modulated at 35 Hz and 45 Hz.
Experiments were carried out for both Active and Passive conditions. In the Active condition, participants were instructed
to describe the two syllable sounds that they heard in the left and right ears between trials. In the Passive condition, participants just watched a silent movie during the dichotic stimulation. Behaviorally, the accuracy of reports to stimulation fromright ear is higher than from left ear (right-ear advantage) for most participants. The amplitude of the ASSR for the rightear input was significantly greater than the left ear input in right ear advantage participants (P < 0.05). This result suggests
the frequency tagging method is a useful tool for exploring auditory neural processing during binaural interaction, such as
dichotic listening.
Keywords: Dichotic listening, Frequency tagging, Magnetoencephalogram (MEG)

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 2020/3
Proceedings of Biological Imaging and Medical AI - JSMBE 2020/3

JSMBE-BIMAI2019-05 (2020-03)

Preliminary Study on Segmentation of Cervical Intervertebral
Disks in Videofluorography by Multi Channelization with Various
Image Filters and CNN
Ayano Fujinaka*, Kojiro Mekata**, Hotaka Takizawa***, Hiroyuki Kudo**** Department of Computer Science, Graduate School of Systems and Information Engineering, University of Tsukuba,
Tsukuba, Japan** Department of Rehabilitation, Kobe Red Cross Hospital, Kobe, Japan** Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, Japan

Abstract
In order to elucidate the mechanism of swallowing, it is necessary to analyze the relationship between swallowing andcervical structures. This study proposes a segmentation method of cervical intervertebral disks (CIDs) in videofluorography(VF) by use of Multi Channelization (MC) and convolutional neural network (CNN). Each frame of VF is a grayscale image.
Forty-five image filters are individually applied to the grayscale image to generate the same number of feature images. In
this report, we use eight Sobel filters, two Gaussian filters, two exponential tone curve filters, and so forth. Three feature
images are selected and are set to the red (R), green (G) and blue (B) channels of a color image, which is called a Multichannelized image. Patch-based CNN is applied to the MC image to extract CID regions. The extraction accuracy is evaluated by an energy function based on the pixel-based F-measure of the CID regions. The most optimal combination of thethree feature images is searched for by the simulated annealing technique. The proposed method is applied to a VF dataset
consisting of nineteen patients with cervical vertebral diseases and thirty-nine healthy participants. 50.4%, 16.8% and 32.8%
of the VF images are used for training, validation, and testing, respectively. LeNet is used as CNN in this report. The
segmentation accuracy is 56.8% when original grayscale images are input into CNN, whereas it is 58.8% when the optimal
combination of feature images are input into CNN. The optimal combination is composed of the sigmoid tone curve filter,the morphological top-hat transformation filter, and the posterization filter with more layers. Figures 1, 2 and 3 show an
original frame image of VF, the ground truth and the segmentation result with MC and CNN, respectively. The experimental
results demonstrated that the MC technique is effective in segmentation of CIDs in VF.
Keywords: Videofluorography, Cervical intervertebral disk, Multi Channelization, Simulated annealing, CNN.

Fig.1

Fig.2

Fig.3

Figures – Fig.1 shows an original frame image of VF. Fig.2 shows the ground truth of cervicalintervertebral disks. Fig.3 shows the segmentation result with Multi Channelization and CNN.

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 2020/3
Proceedings of Biological Imaging and Medical AI - JSMBE 2020/3

JSMBE-BIMAI2019-06 (2020-03)

A feasibility study on predicting synthetic dual-energy X-ray fluoroscopic images using deep convolutional neural network
Jiaoyang WANG*, Kei ICHIJI**, Noriyasu HOMMA*** Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan.
** Tohoku University Graduate School of Medicine, Sendai, Japan.

Abstract
Pinpoint radiation dose delivery to a target tumor based on accurate and precise target localization is an essential task inradiation therapy. To achieve the pinpoint dose delivery, especially for the moving target during the treatment session suchas lung tumors, various X-ray fluoroscopic image-based target tracking methods have been developed. A problem here isthat the X-ray fluoroscopic image-based methods are frequently suffering from poor target visibility since the target tumor
is obscured by other internal organs such as bones. Hence a hardware-based technique called dual-energy (DE) imaging
has been introduced to enhance the visibility of soft-tissues including the target tumor in X-ray images. However, DE imaging technique has a disadvantage in radiation dose reduction and clinically available hardware causes motion artifacts in
DE imaging. To resolve the limitations of the hardware-based DE imaging and achieve accurate tracking of the target tumor during radiation therapy, we investigate the feasibility of a deep learning-based method to predict DE X-ray fluoroscopic images from single-energy X-ray images. In this study, we applied simulated du-al-energy X-ray fluoroscopic images as training datasets and trained recurrent residual convolutional neural networks based on U-Net to respectively predict bone and soft-tissue images from high-energy X-ray fluoroscopic images. The experimental results showed that thepredicted DE images were highly similar to the original DE images. Thus, the proposed method can be a solution to obtain DE X-ray fluoroscopic images without special hardware and extra-dose and then can assist the accurate target tumor
localization.
Keywords: radiation therapy, X-ray fluoroscopic images, dual-energy, U-net.

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 2020/3
Proceedings of Biological Imaging and Medical AI - JSMBE 2020/3

JSMBE-BIMAI2019-07 (2020-03)

Fully Automatic Vertebra Segmentation for Spine and Pelvis
Alignment Analysis in a Large-scale CT Database
Zhuo Cheng*, Yuki Tanaka*, Yoshito Otake*, Masaki Takao**, Keisuke Uemura*, Nobuhiko Sugano**, Yoshinobu Sato** Division of Information Science, Nara Institute of Science and Technology, Nara, Japan.
** Graduate School of Medicine, Osaka University, Osaka, Japan.

Abstract
Our project aims at the analysis of musculoskeletal structures for preoperative surgical planning and prediction of
postoperative changes in orthopaedic surgery, specifically in the hip region. We had previously developed a system for
automated segmentation of pelvis, femur, and their surrounding muscles in CT [1]. Analysis of the alignment of pelvis andspine is important and has a potential for a variety of clinical applications, while the multi-class segmentation of eachvertebrae poses a number of challenges including similarity between the neighboring vertebrae, large variation of the fieldof view (FOV) and the range of scans in the protocols commonly used in clinical practice. Thus, a global rather than a perinstance approach may work better to ensure a reasonable anatomical labeling.
In this work, we employed a multi-stage iterative approach, which is similar to the method proposed by Lessmann etal. [2], for multi-class segmentation of vertebrae using fully convolutional neural networks based on the prior knowledge
that the vertebra is always located next to each other. The prior knowledge allows us to follow the vertebral column fromone end to the other during the segmentation process. Furthermore, we fine-tuned the predicted labels according tocontinuous sequence of vertebrae labels. A 4-fold cross validation study using 74 cases of the public dataset of MICCAI
challenge Verse2019 [3] was conducted. The proposed method was compared with the conventional patch-based 3D Unet. The evaluation was performed by two metrics, dice coefficient and identification accuracy, similar to Lessmann et al.
[2]. Dice coefficient was calculated between a vertebra in the ground truth image and the predicted vertebra that had thelargest overlap without considering the assigned label. The identification was considered as successful if the label matchedwith the ground truth. The
average dice coefficient andidentification accuracy over74 cases (943 vertebrae in
total) was 0.926±0.105 and
89% for the proposed method
as compared to 0.744±0.184
and 83% for the conventional
3D U-net. The application of
the proposed segmentation
method in a large-scale CT
Fig. 1. Heatmap visualization of dice coefficient and identification accuracy for the proposed method anddatabase for the analysis ofconvolutional 3D U-net. The row indicates each patient and the column indicate vertebral level. The sagittal
spine and pelvis alignment isslice on the right shows the representative final segmentation result (Patient #29) marked by the red arrow.
underway.
Keywords: Vertebra segmentation, Instance segmentation, Deep learning
References[1] Hiasa, Y., Y. Otake, M. Takao, T. Ogawa, N. Sugano and Y. Sato (2019). "Automated Muscle Segmentation from Clinical
CT using Bayesian U-Net for Personalized Musculoskeletal Modeling." IEEE Transactions on Medical Imaging (in press).
[2] Lessmann, N., B. van Ginneken, P. A. de Jong and I. Išgum (2019). "Iterative Fully Convolutional Neural Networks for
Automatic Vertebra Segmentation and Identification." Medical Image Analysis 53: 142-155.
[3] Sekuboyina et al. (2020). “VerSe: A Vertebrae Labelling and Segmentation Benchmark.” arXiv preprint arXiv: 2001.0
9193.

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