研究者詳細 - 神谷　亨

論文 - 神谷　亨

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ResNetを用いた指骨CR画像からの骨粗しょう症の自動識別

畠野、村上、植村、陸、タン、金、青木

第37回日本医用画像工学会大会予稿集 238 - 244 2018年07月

3次元Residual networks(ResNets)を用いた大腸CADにおける偽陽性陰影の識別

植村、陸、金、橘、弘中、Nappi、吉田

第37回日本医用画像工学会大会予稿集 436 - 442 2018年07月

CNNによる胸部CT画像からの経時的差分画像上の異常陰影の検出

長尾、植村、三宅、陸、タン、金、村上、青木、平野、木戸

第37回日本医用画像工学会大会予稿集 404 - 410 2018年07月

Image Registration for Detection of Sclerotic Bone Metastasis in CT Images 査読有り国際誌

Kim, Lu, Tan, Murakami, Ueno, Terasawa, TAoki

40th International Conference of the IEEE Engineering in Medicine and Biology Society 2018年07月

3D deelp residual convolutional networks for computer-aided detection of polyos in CT colonography 査読有り

Uemura, Lu, Kim, Tachibana, Hironaka, Nappi, Yoshida

International Journal of Computer Assited Radiology and Surgery 94 - 95 2018年06月

Low illumination underwater light field images reconstruction using deep convolutional neural networks 査読有り国際誌

Lu H., Li Y., Uemura T., Kim H., Serikawa S.

Future Generation Computer Systems 82 142 - 148 2018年05月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 2018 Elsevier B.V. Underwater optical images are usually influenced by low lighting, high turbidity scattering and wavelength absorption. To solve these issues, a great deal of work has been performed to improve the quality of underwater images. Most of them use the high-intensity LEDs for lighting to obtain the high contrast images. However, in high turbidity water, high-intensity LEDs cause strong scattering and absorption. In this paper, we propose a light field imaging approach for solving underwater imaging problems in a low-intensity light environment. As a solution, we tackle the problem of de-scattering from light field images by using deep convolutional neural networks with depth estimation. Furthermore, a spectral characteristic-based color correction method is used for recovering the color reduction. Experimental results show the effectiveness of the proposed method by challenging real-world underwater imaging.

DOI： 10.1016/j.future.2018.01.001

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85040510117&origin=inward

Automatic identification of bone erosions in rheumatoid arthritis from hand radiographs based on deep convolutional neural network 査読有り

Murakami, Hatano, Tan, Kim, Aoki

International Journal of Multimedia Tools and Applications 77 ( 9 ) 10921 - 10937 2018年05月

Active contour model-based segmentation algorithm for medical robots recognition 査読有り

Yujie Li, Yun Li, Kim, Serikawa

Journal of Multimedia Tools and Applications 77 ( 9 ) 10485 - 10500 2018年05月

Detection of lung carcinoma with predominant ground-glass opacity on CT using temporal subtraction method 査読有り

Terasawa, Aoki, Murakami, Kim, Fujii, Kobayashi, Chihara, Hayashida, Korogi

European Radiology 28 ( 4 ) 1594 - 1599 2018年04月

Brain Intelligence: Go beyond Artificial Intelligence 査読有り国際誌

Lu H., Li Y., Chen M., Kim H., Serikawa S.

Mobile Networks and Applications 23 ( 2 ) 368 - 375 2018年04月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 2017, Springer Science+Business Media, LLC. Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan’s economy and solves various social problems. In recent years, AI has attracted attention as a key for growth in developed countries such as Europe and the United States and developing countries such as China and India. The attention has been focused mainly on developing new artificial intelligence information communication technology (ICT) and robot technology (RT). Although recently developed AI technology certainly excels in extracting certain patterns, there are many limitations. Most ICT models are overly dependent on big data, lack a self-idea function, and are complicated. In this paper, rather than merely developing next-generation artificial intelligence technology, we aim to develop a new concept of general-purpose intelligence cognition technology called “Beyond AI”. Specifically, we plan to develop an intelligent learning model called “Brain Intelligence (BI)” that generates new ideas about events without having experienced them by using artificial life with an imagine function. We will also conduct demonstrations of the developed BI intelligence learning model on automatic driving, precision medical care, and industrial robots.

DOI： 10.1007/s11036-017-0932-8

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85029688332&origin=inward

Non-uniform de-Scattering and de-Blurring of Underwater Images 査読有り国際誌

Li Y., Lu H., Li K., Kim H., Serikawa S.

Mobile Networks and Applications 23 ( 2 ) 352 - 362 2018年04月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2017, Springer Science+Business Media, LLC. Optical underwater images often demonstrate low contrast, heavy scatter, and color distortion. Contrast enhancement methods have been proposed to solve these issues. However, such methods typically do not consider high-level inhomogeneous scatter removal and do not focus on real-scene color restoration. We proposed a hierarchical transmission fusion method and a color-line ambient light estimation method for image de-scattering from a single input image. Our proposed method can be summarized into three steps. Firstly, we take the dark channel as prior information to estimating the preliminary transmission and ambient light. In the second step, we then use color lines to estimate the refined ambient light in selected patches. The refined transmission is obtained by hierarchical transmission maps using maximum local energy-based fusion at different turbidity levels. We then use a joint normalized filter to obtain the final transmission. Finally, a chromatic color correction method and de-blurring algorithm are used to recover the scene color. Experimental results demonstrate that the accurate estimation of the depth map and ambient light by the proposed method can recover visually appealing images with sharp details.

DOI： 10.1007/s11036-017-0933-7

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85029692278&origin=inward

DCNNによる指骨CR画像からの骨粗しょう症の自動識別査読有り

畠野、村上、植村、陸、タン、金、青木

Medical Imaging Technology 36 ( 2 ) 90 - 95 2018年03月

経時的差分像技術を用いたGGO陰影の強調表示法

金、近藤、陸、村上、寺澤、青木、平野、木戸

第10回呼吸機能イメージング研究会学術集会抄録集 45 - 45 2018年02月

Detection of a specific moving object from head-mounted camera images 査読有り国際誌

Ishitobi K., Tan J., Kim H., Ishikawa S.

SII 2017 - 2017 IEEE/SICE International Symposium on System Integration 2018-January 817 - 822 2018年02月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2017 IEEE. In this paper, a method is proposed for detecting and tracking a specific moving object (e.g., a bus) on the road from images of a camera attached to the head of a user, aiming at developing a system to support daily lives of visually impaired people. The proposed method traces feature points on the images, extracts a moving object region, and detects a bus by applying Haar-like feature and random trees to the region. The effectiveness of the proposed method is shown experimentally.

DOI： 10.1109/SII.2017.8279323

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85050871522&origin=inward

A unified action recognition framework 査読有り

Amine, Tan, Kim, Ishikawa

he 23rd International Syposium on Artificial Lefe and Robotics 57 - 62 2018年01月

Bone erosions detection on hand CR images based on DCNN 査読有り

Murakami, Hatano, Lu, Tan, Kim, Aoki

The 23rd International Syposium on Artificial Lefe and Robotics 357 - 360 2018年01月

A Diffeomorphic Demons Approach to Statistical Shape Modeling 査読有り国際誌

Li G., Wu J., Xiao Z., Lu H., Kim H., Ogunbona P.

Studies in Computational Intelligence 752 123 - 131 2018年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© Springer International Publishing AG 2018. Automatic segmentation of organs from medical images is indispensable for the applications of computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). Statistical Shape Models (SSMs) based scheme have been proved as the accurate and robust methods for extraction of anatomical structures. A key step of this approach is the need to place the sampled points(landmarks) with correspondence across the training set. On the one hand, the correspondence of landmarks is related the quality of SSMs. On the other hand, in many cases the location of key landmarks should be manipulated by physicians, since an unattended system is hard to use in most clinical applications. In this paper, we establish a dense correspondence across the whole training set automatically by surface features, which are registered using diffeomorphic demons approach. And the optimization is executed on spherical domain. We establish the SSM for lung regions, the deformation of where is greatly. Finally, we derive quantitative measures of model quality and comparison of segmentation results using the model with non-optimized correspondence.

DOI： 10.1007/978-3-319-69877-9_14

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85033680194&origin=inward

Underwater Light Field Depth Map Restoration Using Deep Convolutional Neural Fields 査読有り国際誌

Lu H., Li Y., Kim H., Serikawa S.

Studies in Computational Intelligence 752 305 - 312 2018年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© Springer International Publishing AG 2018. Underwater optical images are usually influenced by low lighting, high turbidity scattering and wavelength absorption. In order to solve these issues, a great deal of work has bee n used to improve the quality of underwater images. Most of them used the high-intensity LED for lighting to obtain the high contrast images. However, in high turbidity water, high-intensity LED causes strong scattering and absorption. In this paper, we firstly propose a light field imaging approach for solving underwater depth map estimation problems in low-intensity lighting environment. As a solution, we tackle the problem of de-scattering from light field images by using deep convolutional neural fields in depth estimation. Experimental results show the effectiveness of the proposed method through challenging real world underwater imaging.

DOI： 10.1007/978-3-319-69877-9_33

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85033692294&origin=inward

Automatic identification of bone erosions in rheumatoid arthritis from hand radiographs based on deep convolutional neural network 査読有り国際誌

Murakami S., Hatano K., Tan J., Kim H., Aoki T.

Multimedia Tools and Applications 77 ( 9 ) 10921 - 10937 2018年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© Springer Science+Business Media, LLC 2017. Although radiographic assessment of joint damage is essential in characterizing disease progression and prognosis in patients with rheumatoid arthritis (RA), it is often difficult even for trained radiologists to find radiographic changes on hand and foot radiographs because lesion changes are often subtle. This paper proposes a novel quantitative method for automatically detecting bone erosion on hand radiographs to assist radiologists. First, the proposed method performs with the crude segmentation of phalanges regions from hand radiograph and extracts the detailed phalanges regions by the multiscale gradient vector flow (MSGVF) Snakes method. Subsequently, the region of interest (ROI; 40 × 40 pixels) is automatically set on the contour line of the segmented phalanges by the MSVGF algorithm. Finally, these selected ROIs are identified by the presence or absence of bone erosion using a deep convolutional neural network classifier. This proposed method is applied to the hand radiographs of 30 cases with RA. The true-positive rate and the false-positive rate of the proposed method are 80.5% and 0.84%, respectively. The number of false-positive ROIs is 3.3 per case. We believe that the proposed method is useful for supporting radiologists in imaging diagnosis of RA.

DOI： 10.1007/s11042-017-5449-4

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85056120147&origin=inward

DCNNによる指骨CR画像からの骨粗しょう症の自動識別査読有り

畠野和裕, 村上誠一, 植村知規, 陸慧敏, タンジュークイ, 金亨燮, 青木隆敏

Ｍｅｄｉｃａｌ　Ｉｍａｇｉｎｇ　Ｔｅｃｈｎｏｌｏｇｙ ( 日本医用画像工学会 ) 36 ( 2 ) 90 - 95 2018年01月

担当区分：責任著者記述言語：日本語掲載種別：研究論文（学術雑誌）

骨のおもな疾患として，骨粗しょう症が挙げられる．骨粗しょう症に対する画像診断は有効であるが，類似した低骨量を呈する画像も多く，画像診断における客観性や再現性の問題がある．そこで本稿では，指骨computed radiography（CR）画像から骨粗しょう症の自動識別手法を提案する．提案手法では，深層畳み込みニューラルネットワーク（DCNN）を用いた識別器を構築し，骨粗しょう症有無の識別を行う．DCNNの学習および識別には，CR画像から3種類の画像を作成し，各指骨領域内部からROIを抽出後，この3種類のROIをR，G，Bチャンネルに割り当て生成した疑似カラー画像を用いる．実験では，101症例に対し提案手法を適用し，真陽性率（TPR）：75.5［％］，偽陽性率（FPR）：13.9［％］という結果を得た．

DOI： 10.11409/mit.36.90

CiNii Article

CiNii Research

その他リンク： https://ci.nii.ac.jp/naid/130006588793

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