研究者詳細 - 神谷　亨

論文 - 神谷　亨

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SEblockとDeformable Convolutionを加えたAlexNetによる胸部CT画像からの結節状陰影の検出

玉井，神谷，青木，木戸

2022年度電気学会電子・情報・システム部門大会 1262 - 1267 2022年08月

Geometric Ｍatching CNNによる指骨CR画像の位置合わせ手法

小野、神谷、青木

第41回日本医用画像工学会大会予稿集 84 - 85 2022年07月

臨床情報を加えた改良型 ResNet による CT 画像からの結節状陰影の識別

本田直也，神谷亨，木戸尚治

第41回日本医用画像工学会大会予稿集 176 - 177 2022年07月

LPCとHPSSを用いた呼吸音の自動分類

田端、陸、神谷、間普、木戸

第41回日本医用画像工学会大会予稿集 180 - 181 2022年07月

LightGBMにRadiomics特徴量を加味した胸部CT画像からのドライバー遺伝子変異有無の識別

渡邊、神谷、寺澤、青木

電子情報通信学会医用画像研究会、信学技報MI2022-11 61 - 66 2022年05月

Deep CNNを用いた低線量CT画像のデノイジング処理

貞松、村上、李、神谷

電子情報通信学会医用画像研究会、信学技報MI2022-11 67 - 70 2022年05月

Segmentation of Lung Nodules on CT Images Using a Nested Three-Dimensional Fully Convolutional Network 査読有り国際誌

Shoji Kido, Shunske Kidera, Yasushi Hirano, Shingo Mabu, Tohru Kamiya, Nobuyuki Tanaka, Yuki Suzuki, Masahiro Yanagawa, Noriyuki Tomiyama

Frontiers in Artificial Intelligence 5 2022年02月

記述言語：英語掲載種別：研究論文（学術雑誌）

In computer-aided diagnosis systems for lung cancer, segmentation of lung nodules is important for analyzing image features of lung nodules on computed tomography (CT) images and distinguishing malignant nodules from benign ones. However, it is difficult to accurately and robustly segment lung nodules attached to the chest wall or with ground-glass opacities using conventional image processing methods. Therefore, this study aimed to develop a method for robust and accurate three-dimensional (3D) segmentation of lung nodule regions using deep learning. In this study, a nested 3D fully connected convolutional network with residual unit structures was proposed, and designed a new loss function. Compared with annotated images obtained under the guidance of a radiologist, the Dice similarity coefficient (DS) and intersection over union (IoU) were 0.845 ± 0.008 and 0.738 ± 0.011, respectively, for 332 lung nodules (lung adenocarcinoma) obtained from 332 patients. On the other hand, for 3D U-Net and 3D SegNet, the DS was 0.822 ± 0.009 and 0.786 ± 0.011, respectively, and the IoU was 0.711 ± 0.011 and 0.660 ± 0.012, respectively. These results indicate that the proposed method is significantly superior to well-known deep learning models. Moreover, we compared the results obtained from the proposed method with those obtained from conventional image processing methods, watersheds, and graph cuts. The DS and IoU results for the watershed method were 0.628 ± 0.027 and 0.494 ± 0.025, respectively, and those for the graph cut method were 0.566 ± 0.025 and 0.414 ± 0.021, respectively. These results indicate that the proposed method is significantly superior to conventional image processing methods. The proposed method may be useful for accurate and robust segmentation of lung nodules to assist radiologists in the diagnosis of lung nodules such as lung adenocarcinoma on CT images.

DOI： 10.3389/frai.2022.782225

Scopus

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Automatic Segmentation of Finger Bone Regions from CR Images Using Improved U-Net and MSGVF Snakes 査読有り国際誌

Kawagoe, Murakami, Kamiya, Aoki

ICIC Express Letters Part B: Applications 13 ( 2 ) 155 - 160 2022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）

Bone disease including rheumatoid arthritis and osteoporosis is one of se-rious diseases especially elderly people. Diagnostic imaging using computed radiography (CR) imaging is an effective method for diagnosing the bone disease. However, accurate diagnosis should be made within a limited time, increasing the burden on doctors. There-fore, the goal of this paper is to develop a computer aided diagnosis (CAD) system for automatically diagnosing bone disease from CR images. The proposed method based on an improved U-Net and multiscale gradient vector flow snakes (MSGVF Snakes) extracts each phalange region from the CR image and creates an image for each phalange. In the experiment, the proposed method was applied to CR images of 101 cases, and the performance was evaluated. As a result, our proposed model based on improved U-Net for the segmentation obtained intersection over union (IoU) of 0.939, true positive (TP) of 0.984, and false positive (FP) of 0.068. The initial segmentation accuracy was higher than the conventional approach such as U-Net, Residual U-Net. Final segmentation performance of the phalanges based on MSGVF Snakes are TP of 0.945 and FP of 0.030.

DOI： 10.24507/icicelb.13.02.155

Scopus

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深層距離学習における誤差関数の改良によるパノラマエックス線画像からの歯根吸収の検出

田村、神谷、小田、森本

北九州医工学術者会議 2022年01月

画像処理技術による肺癌のコンピュータ支援診断招待有り

神谷

第13回呼吸機能イメージング研究会学術集会抄録集 27 2022年01月

Liver Segmentation in CT Images Using Deep-Learning and 3D CRF 査読有り国際誌

Nagano, Kamiya, Li

International Conference on Artificial Life and Robotics 540 - 53 2022年01月

Segmentation of liver tumors in multiphase computed tomography images using hybrid method 査読有り国際誌

Wu, Furuzuki, Li, Kamiya, Mabu, Tanabe, Ito, Kido

International Journal of Computer and Electrical Engineering 97 1 - 11 2022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）

The multiphase scan has enabled an improved detection of liver tumors. However, tumor regions and peripheral tissues are difficult to distinguish and delineate owing to their highly similar image features. Moreover, their characteristics vary significantly in different phases. This is challenging when using segmentation methods that are based on unique training models. Herein, a hybrid framework is proposed for liver tumor segmentation in multiphase images. We first develop a cascade region-based convolutional neural network with refined head to locate the tumors. Meanwhile, phase-sensitive noise filtering is introduced to refine the segmentation conducted by a level-set-based framework. This method is sensitive to the intensity contrast but not to the regions of interest, thereby affording better performance in delineating adjacent tumors. In our experiment, the average precision and recall rates are 76.8% and 84.4%, respectively. The intersection over union, true positive rate, and false positive rate are 72.7%, 76.2%, and 4.75%, respectively.

DOI： 10.1016/j.compeleceng.2021.107626

Scopus

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Underwater Video Networking and Target Tracking 査読有り国際誌

Ota H., Lu H., Li J., Zheng Y., Kamiya T., Nakatoh Y., Serikawa S.

Proceedings of SPIE - The International Society for Optical Engineering 12508 2022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）

IoT technology has made remarkable progress in recent years, and the world is full of IoT devices that continue to evolve every day. From smartphones, personal computers, and smartwatches to home appliances such as refrigerators and washing machines, and even indoor lights and house keys, IoT devices have become an inseparable part of our lives. In addition to devices used by individuals, IoT technology supports our daily lives from both front and back sides, such as IoT-enabled industrial equipment and satellite positioning systems. Japan has been making a national push to shift to IoT in industries that reduce the burden on workers and have recently been promoting a plan called Smart Agriculture, Forestry, and Fisheries. Among these three types of industries, the agricultural sector is slightly ahead of the others, with the Smart Agriculture Demonstration Project starting in 2019, and 182 districts in Japan are implementing the project by FY2021. The forestry and fisheries industries are also developing daily to become next-generation industries based on the program established in December 2019, although they are behind agriculture. However, the examples mentioned so far are those that have been promoted with the help of companies and the national government, although everyone has benefited from them. In this paper, we propose an IoT device that can be used as an IoT buoy by using a microcomputer, Raspberry Pi, to create a camera device that can distribute underwater images in real-time and track its location.

DOI： 10.1117/12.2655052

Scopus

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Underwater Image Super-Resolution Using Improved SRCNN 査読有り国際誌

Horimachi R., Lu H., Zheng Y., Kamiya T., Nakatoh Y., Serikawa S.

Proceedings of SPIE - The International Society for Optical Engineering 12508 2022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）

In recent years, industrialization and economic development in countries around the world have led to an ever-increasing demand for energy. Renewable energies are attracting attention, but they still often use mineral resources such as coal, petroleum, and natural gas, and onshore resources are depleting day by day. These energy and metal resources, such as copper, support Japan's industries and affluent lifestyle, and if Japan continues to rely on imports for most of these resources, it will become difficult for Japan to secure a stable supply of these energies and resources. Therefore, mining of mineral resources on the seafloor is essential to solve these problems, and research on seafloor resource surveys and mining is underway. Because direct human exploration and mining of seafloor resources are naturally dangerous, underwater robots are used to explore and mine seafloor resources. However, due to light absorption and turbidity in water, the underwater image of an underwater robot is sometimes less visible, making exploration unsatisfactory. Therefore, there is a need for higher-resolution underwater images of underwater robots. In this study, we perform super-resolution of underwater images using an improved SRCNN to support research on underwater images of underwater robots. The conventional SRCNN method uses the ReLU function as the activation function, but the improved SRCNN uses the PReLU function and FReLU function, which are extended activation functions of the ReLU function, to improve accuracy.

DOI： 10.1117/12.2655051

Scopus

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Three-Dimensional Quantification of Postoperative Facial Asymmetry in Patients With Unilateral Cleft lip and Palate Using Facial Symmetry Plane 査読有り国際誌

Shiigi A., Okawachi T., Kamiya T., Hosoki D., Nomoto N., Ratman M.F., Amir M.S., Ishihata K., Nakamura N.

Cleft Palate-Craniofacial Journal 2022年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

Objective: To quantitatively assess three-dimensional (3D) soft tissue facial asymmetry in patients with unilateral cleft lip and palate (UCLP) who have undergone primary lip repair. Design: Clinical, retrospective, comparative, methodological study. Patients/Participants: Twenty patients with UCLP were selected after a review of the records. Inclusion Criteria: Complete UCLP; surgically treated without secondary repair. An age-matched and sex-matched Control group was employed. Main Outcome Measures: A 3D facial symmetry plane (FSP) was obtained by superimposing the point clouds of the original 3D facial image excluding the surgical site and including lip and nose areas and those of a mirrored facial image using the iterative closest point (ICP) adjustment method. The discrepancies in the depth and angle of the normal vector of the facial surface of each point cloud between right and left sides (cleft and non-cleft sides in the UCLP group, respectively) based on FSP were calculated. Results: Facial asymmetry in the UCLP group was significantly greater than in the Control group regarding both the discrepancies in the depth (1.34 ± 0.62, 0.73 ± 0.32 pixels, respectively) (P =.0004) and surface angle (18.0 ± 5.88, 12.8 ± 4.0°, respectively) (P =.0024). Biaxial assessment of the discrepancies in the depth and surface angle allowed us to visually extract UCLP patients with greater facial asymmetry. Conclusions: Facial asymmetry analysis based on 3D FSP effectively facilitates the facial asymmetry quantification and soft tissue surgical outcome evaluation in patients with UCLP.

DOI： 10.1177/10556656221123276

Scopus

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

Teng Y., Lu H., Li Y., Kamiya T., Nakatoh Y., Serikawa S., Gao P.

IEEE Transactions on Intelligent Transportation Systems 24 ( 4 ) 4532 - 4540 2022年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

In the process of transportation, the handling and loading methods of rigid objects are becoming more and more perfect. However, whether in today's transportation system or in daily life, such as packing objects or sorting cables before transportation, the manipulation of deformable objects has been always inevitable and has attracted more and more attention. Due to the super degrees of freedom and the unpredictable physical state of deformed objects. It is difficult for robots to complete tasks under the environment of the deformable object. Therefore, we present a method based on imitation learning. In the generated expert demonstration, the agent is offered to learn the state sequence, and then imitate the expert's trajectory sequence which avoid the above-mentioned difficulties. In addition, compared with the baseline method, our proposed DN-Transporter Networks are more competitive in a simulation environment involving cloth, ropes or bags.

DOI： 10.1109/TITS.2022.3168303

Scopus

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Grasping Position Estimation Method Using Depth Image for Thin Objects 査読有り

Yoshihara T., Koga S., Lu H., Kamiya T.

Proceedings of SPIE - The International Society for Optical Engineering 12508 2022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）

The robot market in Japan is gradually expanding due to increasing demand. Industrial robots are being actively introduced in the manufacturing industry. The introduction of robots in the industry has three advantages: 1) securing labor, 2) increasing productivity, and 3) improving quality. The robot can run for a long time with constant work efficiency, thus achieving stable production. In addition, by replacing human labor, robots can reduce labor costs and reduce human error. The downside of introducing a robot is that the robot has to be told where to grab, which takes time, and a technician with specialized knowledge. Furthermore, this method cannot perform grasping when the grasping object is not in the specified position. However, the introduction of robot vision may solve these problems. In this study, by using depth images, processed images, and deep learning models, we aim to achieve object color-independent high-accuracy grasp position estimation for thin objects. We sharpen the depth image mainly by applying grayscale transformation and modify the deep learning model. The experimental results show that our design can achieve good results.

DOI： 10.1117/12.2663251

Scopus

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Grasp Position Estimation from Depth Image Using Stacked Hourglass Network Structure 査読有り

Hamamoto K., Lu H., Li Y., Kamiya T., Nakatoh Y.O., Serikawa S.

Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022 1188 - 1192 2022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）

In recent years, robots have been used not only in factories. However, most robots currently used in such places can only perform the actions programmed to perform in a predefined space. For robots to become widespread in the future, not only in factories, distribution warehouses, and other places but also in homes and other environments where robots receive complex commands and their surroundings are constantly being updated, it is necessary to make robots intelligent. Therefore, this study proposed a deep learning grasp position estimation model using depth images to achieve intelligence in pick-and-place. This study used only depth images as the training data to build the deep learning model. Some previous studies have used RGB images and depth images. However, in this study, we used only depth images as training data because we expect the inference to be based on the object's shape, independent of the color information of the object. By performing inference based on the target object's shape, the deep learning model is expected to minimize the need for re-training when the target object package changes in the production line since it is not dependent on the RGB image. In this study, we propose a deep learning model that focuses on the stacked encoder-decoder structure of the Stacked Hourglass Network. We compared the proposed method with the baseline method in the same evaluation metrics and a real robot, which shows higher accuracy than other methods in previous studies.

DOI： 10.1109/COMPSAC54236.2022.00187

Scopus

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3D object detection using improved PointRCNN 査読有り

Fukitani K., Shin I., Lu H., Yang S., Kamiya T., Nakatoh Y., Serikawa S.

Cognitive Robotics 2 242 - 254 2022年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

Recently, two-dimensional object detection (2D object detection) has been introduced in numerous applications such as building exterior diagnosis, crime prevention and surveillance, and medical fields. However, the distance (depth) information is not enough for indoor robot navigation, robot grasping, autonomous running, and so on, with conventional object detection. Therefore, in order to improve the accuracy of 3D object detection, this paper proposes an improvement of Point RCNN, which is a segmentation-based method using RPNs and has performed well in 3D detection benchmarks on the KITTI dataset commonly used in recognition tasks for automatic driving. The proposed improvement is to improve the network in the first stage of generating 3D box candidates in order to solve the problem of frequent false positives. Specifically, we added a Squeeze and Excitation (SE) Block to the network of pointnet++ that performs feature extraction in the first stage and changed the activation function from ReLU to Mish. Experiments were conducted on the KITTI dataset, which is commonly used in research aimed at automated driving, and an accurate comparison was conducted using AP. The proposed method outperforms the conventional method by several percent on all three difficulty levels.

DOI： 10.1016/j.cogr.2022.12.001

Scopus

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Recognition of Sidewalk Environment Based on WideSegPlus 査読有り国際誌

Sakai Y., Lu H., Li Y., Kamiya T.

Proceedings of SPIE - The International Society for Optical Engineering 12508 2022年01月

担当区分：最終著者,　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 recent years, the elderly population in Japan has been increasing. Expectations for the utilization of welfare equipment are also increasing. Electric wheelchairs are one of equipment and are widely used as a convenient means of transportation. On the other hand, accidents have also occurred, and dangers have been pointed out when driving the electric wheelchair. Therefore, we believe that the development of an autonomous mobile electric wheelchair can improve the causes of accidents. In addition, it can be expected to reduce accidents and improve the convenience of electric wheelchairs. For the development of an autonomous electric wheelchair, environment recognition such as estimation of the current position, recognition of sidewalks and traffic lights, and prediction of movement of objects is indispensable. To solve these problems, we develop an algorithm to recognize the sidewalks, crosswalks, and traffic lights from video images. In recent years, deep learning has been widely applied in the field of image recognition. Therefore, we improve WideSeg, one of the semantic segmentation algorithms that apply CNN (Convolutional Neural Networks), and develop an object recognition method using a new CNN model. In our approach, we perform adding the sidewalk correction and noise removal processing after performing semantic segmentation with the proposed model.

DOI： 10.1117/12.2655680

Scopus

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