研究者詳細 - 神谷　亨

論文 - 神谷　亨

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FPA LRSE U-Netを用いたCT画像からの脊椎領域の自動抽出

木佐貫　航平，神谷　亨，寺澤　岳，青木

第42回日本医用画像工学会大会予稿集 72 - 73 2023年09月

AI技術とコンピュータ支援診断への応用招待有り査読有り

神谷

画像ラボ 3 ( 8 ) 29 - 34 2023年08月

3次元点群動画像を用いた顔の時系列変化の検出および非対称度評価

木原　成海，野元　菜美子，大河内　孝子，李　光旭, 中村　典史, 神谷:

第42回日本医用画像工学会大会予稿集 217 - 218 2023年07月

3DCNNを用いた脳MR画像の3次元位置合わせ

馬場　星太郎，神谷

第42回日本医用画像工学会大会予稿集 227 - 228 2023年07月

非侵襲的なドライバー遺伝子変異有無の識別のためのRadiomics特徴量に基づく2クラス分類

渡邊，神谷，寺澤，青木

第42回日本医用画像工学会大会予稿集 219 - 220 2023年07月

Res BCDU-SCB Net: A Lung CT Image Segmentation based on SC-BLSTM 査読有り国際誌

Wang T., Li G（天津工大）., Tang C（天津工大）., Kamiya T.

ACM International Conference Proceeding Series 42 - 46 2023年07月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Due to the unique physiological structure of human lung tissue, segmentation of the lung parenchymal regions is a very challenging task. Traditional image segmentation methods have certain limitations on practical applications. Therefore, lung tissue segmentation methods based on deep learning have become a research hotspot in recent years. Res BCDU-Net may overcome the problem of high false positives in other semi-automatic segmentation, but it still has the problem of low accuracy in edge segmentation, which is probably caused by the unclear input of a single CT image. Therefore, we propose to utilize three consecutive CT images as the input, which employs inter-slice context to restrain the false positive. When a single input CT image is not clear, the accuracy of lung segmentation results can be improved by referring to its previous and next slices. Based on the input of three continuous images, the spatial information in the process of lung segmentation is added in this paper. We build a Res BCDU-SCB network and collect and integrate spatial information through SC-BLSTM module. An SC-attention module is designed to take both finer-grained spatial information and rich semantic information into account. In this paper, we append BLSTM (Bidirectional Convolutional Long Short-term Memory) module with SC-attention module, named SC-BLSTM, to decrease performance degradation caused by ambiguous boundaries and variable lung parenchymal shapes. The results of extensive experiments on lung CT images (LIDC-IDRI database) show that it may improve the final segmentation accuracy and reduce the false positives, which confirms the effectiveness of our proposed method.

DOI： 10.1145/3613307.3613316

Scopus

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Temporal subtraction technique for phalanges CR images using U-Net and Geometric-matching CNN 査読有り国際誌

Ono, Kamiya, Aoki

Intl. Journal of Computer Assisted Radiology and Surgery 2023 S84 - S85 2023年06月

Multidimensional Deformable Object Manipulation Based on DN-Transporter Networks 査読有り国際誌

Teng, Huimin Lu, Yujie Li, Tohru Kamiya, Yoshihisa Nakatoh, Serikawa Seiichi, Pengxiang Gao

IEEE Transactions on Intelligent Transportation Systems 24 ( 4 ) 4532 - 4540 2023年04月

Research on adaptive formation control of mobile robot based on improved virtual spring method 査読有り国際誌

Chen Y(天津工大)., Li B., Kamiya T., Shi X.

ACM International Conference Proceeding Series 775 - 780 2023年03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）

A robot formation control algorithm based on virtual spring model is proposed to solve the problem of path planning and formation keeping in unknown environment. By improving the design of target attraction, obstacle repulsion and interaction force between robots, the formation control is more secure and stable. Furthermore, the leader-follower formation transformation problem is studied. According to the objective function of minimum completion time, minimum total energy consumption and minimum total spring deformation, an adaptive formation transformation strategy is designed. This strategy can choose formation transformation mode adaptively according to the environment characteristics, such as adjust the parameters of the formation and reconfigure the formation, so that the robot formation can complete the task in a short time and energy consumption. Finally, the effectiveness of the proposed method is verified by simulation and real experiment.

DOI： 10.1145/3594315.3594404

Scopus

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Volumetric choroidal segmentation using 3D residual U-Net 査読有り国際誌

Li G(天津工大)., Wang K(天津工大)., Wang X(天津工大)., Sun B(天津工大)., Wang K(天津工大)., Gao Y(天津工大)., Sun S(天津工大)., Kamiya T., Dai Y(天津工大).

ACM International Conference Proceeding Series 145 - 149 2023年03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）

Estimating dimensional measurements of the choroid provides diagnostic values which can be used to assess choroidal health. In this paper we describe a methodology of calculating measurements from choroid segmentations automatically generated using convolutional neural network (CNN). We use a three-dimensional (3D) U-Net architecture built from residual units to segment the choroid. A surface fitting phase is jointed to the main process to compensate segmented defects at the area of Optic Nerve Hypoplasia (ONH). Consequently, we process these segmentations to estimate the mean choroidal thickness(MCT). The model is evaluated on volumetric scans from 183 subjects, approximately half of which are thyroid eyes. In the choroidal layer segmentation experiment, the accuracy of the automatic segmentation algorithm proposed in this paper was 98.25% when comparing the manual segmentation results masked by doctors. It showed that the MCT in thyroid eyes were higher than those in normal eyes.

DOI： 10.1145/3594315.3594337

Scopus

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Research on Semantic segmentation co-evolutionary method for Automated Driving Haze Images 査読有り国際誌

Tang C（天津工大）., Wang Y., Kamiya T., Li Y.

ACM International Conference Proceeding Series 157 - 163 2023年03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）

In this paper, we address the problem of semantic segmentation of advanced automatic driving image in hazy weather. We mainly adopt the co-evolution method of image defogging model and semantic segmentation model, the defogging model adopts generative adversarial GAN network, the semantic segmentation model adopts BiSeNet,and the training data is a public self-driving data set Cityscapes. The co-evolution of these two models is used to improve the accuracy of semantic segmentation of fog maps. The experimental results show that the semantic segmentation of fog map by co-evolution has a more obvious improvement effect, and to a certain extent, it can meet the target extraction and recognition of autonomous driving requirements.

DOI： 10.1145/3594315.3594339

Scopus

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Automatic Classification Method for Plastic Bottles and Caps Using Multi Attention Eff-UNet 査読有り国際誌

Moritsuka, Kamiya

International Conference on Artificial Lefe and Robotics 980 - 93 2023年02月

CNNを用いた磁粉探傷画像の分類法査読有り

森塚、神谷

バイオメディカル・ファジィ・システム学会誌 24 ( 2 ) 35 - 42 2023年02月

Atomatic Identification of Tumor Cells for Circulating Tumor Cells by Convolutional Neural Netwokrs 査読有り国際誌

Hashimoto, Kamiya, Li（天津工大）, Yoneda, Tanaka

International Journal of Innovative Computing, Information and Control 19 ( 1 ) 1 - 14 2023年02月

AI技術の医用画像解析への応用招待有り査読有り

神谷

計測と制御 62 ( 2 ) 95 - 97 2023年02月

AUTOMATIC IDENTIFICATION OF TUMOR CELLS FOR CIRCULATING TUMOR CELLS BY CONVOLUTIONAL NEURAL NETWORKS 査読有り国際誌

Hashimoto K., Kamiya T., Li G（天津工大）., Yoneda K., Tanaka F.

ICIC Express Letters 19 ( 1 ) 1 - 14 2023年02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）

Liquid biopsy allows non-invasive collection of circulating tumor cells (CTCs) in blood without the need for sampling and can clearly demonstrate their presence in many types of cancer. In this study, we propose a method to automatically identify CTCs from fluorescence microscopy images and enable quantitative analysis based on convolutional neural networks (CNN). In this paper, a cell nucleus region cropping algorithm is applied in addition to a filtering process centered on a selective enhancement filter. Next, identification by SqueezeNet is performed. We performed the proposed method to 5,040 images of 6 samples and conducted experiments to identify CTCs. The number of detected CTCs was 148 (TPR = 100%), and the number of over-detected non-CTCs was 925. For the identification, TPR = 88.51% and FPR = 5.102% for the CNN model using SqueezeNet. The proposed method successfully reduced the number of detections by about 71.4% without missing any correct answers, but the proposed method did not show good results in any of the evaluation metrics.

DOI： 10.24507/ijicic.19.01.1

Scopus

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深層学習による経時的差分像からの結節状陰影の検出

神谷，芳野，寺澤，青木

第１４回呼吸機能イメージング研究会学術集会 70 - 70 2023年01月

Automatic Classification Method for Plastic Bottles and Caps Using Multi Attention Eff-UNet 査読有り国際誌

Moritsuka S., Kamiya T.

Proceedings of International Conference on Artificial Life and Robotics 980 - 983 2023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（国際会議プロシーディングス）

In Japan, increasing amounts of waste are becoming a social problem. One of approaches to solve the problem is recycle of the plastic bottles. However, they are thrown away with their caps still attached, and it should be removed by hand. To solve this problem, we developed a method for automatic identification of plastic bottles and caps using deep learning technique. In this paper, we propose a method that combines different numbers of Efficient blocks and adds an attention structure and verify its usefulness through experiments.

Scopus

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Squeeze-and-Excitation Block Based Mask R-CNN for Object Instance Segmentation 査読有り

Nagasawa K., Ishiyama S., Lu H., Kamiya T., Nakatoh Y., Serikawa S., Li Y.

Communications in Computer and Information Science 1732 CCIS 56 - 64 2023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）

Deep learning-based methods have taken center stage in image recognition, such as AlexNet and deep learning-based method. At present, Image recognition based on deep learning has been widely used in agriculture, factory automation, automated driving, medical fields and so on. In the fields of automated driving and medical care, the accuracy of the image recognition directly affects human lives. For these reasons, the importance of improving the accuracy of image recognition is clear. In this paper, we focus on instance segmentation tasks. The method used is Mask R-CNN, which is the basis of current state-of-the-art methods. The network structure based on ResNet, and we tried to improve the accuracy by adding Squeeze-and-Excitation Block (SE Block). According to the result of experiments, it is proved that this method has certain advantages for object instance segmentation.

DOI： 10.1007/978-981-99-2789-0_5

Scopus

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Pose Estimation of Point Sets Using Residual MLP in Intelligent Transportation Infrastructure 査読有り国際誌

Li Y., Yin Z., Zheng Y., Lu H., Kamiya T., Nakatoh Y., Serikawa S.

IEEE Transactions on Intelligent Transportation Systems 2023年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

6D pose estimation of arbitrary objects is a crucial topic for intelligent transportation infrastructure measurement. However, some external environmental factors and the characteristics of the object itself impact the accuracy of the object’s pose estimation in practical applications. In this paper, we propose a new multi-class dataset ICD-4 (Industrial car Components Dataset) for 6D object pose estimation, which mainly includes four component categories, and every category takes 20,000 different scenarios. ICD-4 dataset delivers quite a few research challenges involving the range of object pose transformations and has significant research value for small-scale pose estimation tasks. We also propose an innovative method PoseMLP, a pose estimation network that uses residual MLP (multilayer perceptron) modules to predict the 6D pose estimation directly. Simultaneously, the experimental results demonstrate the effectiveness and reliability of the proposed method.

DOI： 10.1109/TITS.2023.3250604

Scopus

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