研究者詳細 - 神谷　亨

論文 - 神谷　亨

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Characteristics based visual servo for 6DOF robot arm control 査読有り国際誌

Tsuchida, Lu, Kamiya, Serikawa

Cognitive Robotics ( Cognitive Robotics ) 1 76 - 82 2021年06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）

Visual servo is a method for robot arm motion control. It is controlled by the end effector velocity that is the result in calculating the internal Jacobi matrix and vector of feature error. In general, automatic robotic task require high quality sensor that can measure a 3-dimential distance, and do calibration in order to suit the sensor frame and robot frame in Euclidean space. In this paper, we only use RGB camera as the data collection, which not requiring the calibration in sensor frame. Thus, our method is simpler than any other automatic motion methods. Meanwhile, the proposed characteristics based visual servo method has varying the hyper parameter, and show the effectiveness for indicating the precision of pose error both simulation and actual environments.

DOI： 10.1016/j.cogr.2021.06.002

Scopus

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Optimizing early cancer diagnosis and detection using a temporal subtraction technique 査読有り

Miyake N., Lu H., Kamiya T., Aoki T., Kido S.

Technological Forecasting and Social Change 167 2021年06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）

To optimize the early diagnosis and detection of lung cancer, computer-aided diagnostic (CAD) systems have been a useful tool for analyzing medical images. The temporal subtraction technique, which is a CAD system, performs the subtraction operation between the current image and the previous image on the same patient, and supports observation by emphasizing the temporal changes. However, the temporal subtraction technique for 3D images, such as thoracic CT images, has not yet been established. There is a need to develop efficient and highly accurate 3D nonrigid registration techniques to reduce subtraction artifacts. This study aims to develop a 3D nonrigid registration technique to establish a 3D temporal subtraction technique. In particular, we focus on the Finite Element Method, which is versatile, applicable to a wide range of fields, and capable of handling any shape. Our new method was examined on 46 clinical cases with multidetector row computed tomography images. As a result, the proposed method improved by 6.93% (p = 3.0 × 10−6) compared to the conventional methods in terms of the rate of reduction of artifacts, and the effectiveness was verified. Therefore, this study contributes to the literature on early detection and treatment.

DOI： 10.1016/j.techfore.2021.120745

Scopus

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CT temporal subtraction: Techniques and clinical applications 招待有り査読有り

Aoki T., Kamiya T., Lu H., Terasawa T., Ueno M., Hayashida Y., Murakami S., Korogi Y.

Quantitative Imaging in Medicine and Surgery 11 ( 6 ) 2021年06月

WideSegNeXt: Semantic Image Segmentation Using Wide Residual Network and NeXt Dilated Unit 査読有り

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

IEEE Sensors Journal 21 ( 10 ) 11427 - 11434 2021年05月

記述言語：英語掲載種別：研究論文（学術雑誌）

Semantic segmentation is widely applied in autonomous driving, in robotic picking, and for medical purposes. Due to the breakthrough of deep learning in recent years, the fully convolutional network (FCN)-based method has become the de facto standard in semantic segmentation. However, the simple FCN has difficulty in capturing global context information, since the local receptive field is small. Furthermore, there is a problem of low image resolution because of the existence of the pooling layer. In this paper, we address the shortcomings of the FCN by proposing a new architecture called WideSegNeXt, which captures the image context on various spatial scales and is effective in identifying small objects. In addition, there is little loss of position information, since there are no pooling layers in the structure. The proposed method achieves a mean intersection over union (MIoU) of 72.5% and a global accuracy (GA) of 92.4% on the CamVid dataset and achieves higher performance than previous methods without additional input datasets.

DOI： 10.1109/JSEN.2020.3008908

Scopus

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U survival for prognostic prediction of disease progression and mortality of patients with COVID 19 査読有り国際誌

Janne J. Näppi, Tomoki Uemura, Chinatsu Watari, Toru Hironaka, Tohru Kamiya, Hiroyuki Yoshida

Nature Scientific Reports 11 ( 1 ) 2021年04月

HPSSを用いた呼吸音の自動分類

丸橋優生、神谷、間普、木戸

信学技報MI2020-77 128 - 133 2021年03月

近未来の医療～コンピュータ画像支援診断未来医療招待有り

神谷

純真の翼 ( 純真学園大学機関紙 ) 7 - 7 2021年03月

A supervoxel classification based method for multi-organ segmentation from abdominal ct images 査読有り

Wu J., Li G., Lu H., Kamiya T.

Journal of Image and Graphics(United Kingdom) 9 ( 1 ) 9 - 14 2021年03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）

Multi-organ segmentation is a critical step in Computer-Aided Diagnosis (CAD) system. We proposed a novel method for automatic abdominal multi-organ segmentation by introducing spatial information in the process of supervoxel classification. Supervoxels with boundaries adjacent to anatomical edges are separated from the image by using the Simple Linear Iterative Clustering (SLIC) from the images. Then a random forest classifier is built to predict the labels of the supervoxels according to their spatial and intensity features. Thirty abdominal CT images are used in the experiment of segmentation task for spleen, right kidney, left kidney, and liver region. The experiment result shows that the proposed method achieves a higher accuracy of segmentation compares to our previous model-based method.

DOI： 10.18178/joig.9.1.9-14

Scopus

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Image Registration Method for Chest MDCT Images Based on 2-D Finite Element Method 査読有り

Ogimoto, Kamiya, Aoki

International Conference on Artificial Life and Robotics 144 - 147 2021年01月

Detection of Abnormal Shadows in Low-dose CT Images Using CNN 査読有り

Ikeda, Kamiya, Aoki

International Conference on Artificial Life and Robotics 148 - 151 2021年01月

Deep-Learning Based Segmentation Algorithm for Defect Detection in Magnetic Particle Testing Images 査読有り

Ueda, Lu, Kamiya

International Conference on Artificial Life and Robotics 235 - 238 2021年01月

Unsupervised survival prediction model from CT images of patients with COVID-19 査読有り

Uemura T., Näppi J.J., Watari C., Kamiya T., Yoshida H.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE 11597 2021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）

We developed an image-based unsupervised survival prediction model, called pix2surv, based on a conditional generative adversarial network (cGAN), and evaluated its performance based on chest CT images of patients with the coronavirus disease 2019 (COVID-19). The architecture of the pix2surv model includes a time generator that consists of an encoding convolutional network and a fully connected prediction network, and a discriminator network. The time generator is trained to generate survival-time images from chest CT images of each patient. The discriminator is a patch-based convolutional network that is trained to differentiate between "fake pairs"of a chest CT image and a generated survival-time image from "true pairs"of the chest CT image and the corresponding observed survival-time image of the patient. For evaluation, we retrospectively collected high-resolution chest CT images of COVID-19 patients. The survival predictions of the pix2surv model on these patients were compared with those of existing clinical prognostic biomarkers by use of a two-sided t-test with bootstrapping. Concordance index (C-index) and relative absolute error (RAE) were used as measures of the prediction performance. The bootstrap evaluation yielded C-index and RAE values of 80.4% and 15.6% for the pix2surv model, whereas those for the extent of the well-aerated lung parenchyma were 49.8% and 33.6%, and for a combination of blood tests of lactic dehydrogenase, lymphocyte, and C-reactive protein were 69.8% and 25.5%, respectively. The increase in survival prediction by the pix2surv model was statistically significant (p < 0.0001), indicating high effectiveness of the pix2surv model as a prognostic biomarker for the survival of patients with COVID-19.

DOI： 10.1117/12.2581929

Scopus

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U-radiomics for predicting survival of patients with COVID-19 査読有り

Uemura T., Näppi J., Watari C., Kamiya T., Yoshida H.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE 11601 2021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）

We developed and evaluated the effect of deep radiomic features, called U-radiomics, on the prediction of the overall survival of patients with the coronavirus disease 2019 (COVID-19). A U-net was trained on chest CT images of patients with interstitial lung diseases to classify lung regions of interest into five characteristic lung tissue patterns. The trained Unet was applied to the chest CT images of patients with COVID-19, and a U-radiomics vector for each patient was identified from the bottleneck layer of the U-net across all the axial CT images of the patient. The U-radiomics vector was subjected to a Cox proportional hazards model with an elastic-net penalty for predicting the survival of the patient. The evaluation was performed by use of bootstrapping, where the concordance index (C-index) was used as the comparative performance metric. Our preliminary comparative evaluation of existing prognostic biomarkers and the proposed U-survival model yielded the C-index values of (a) extent of well-Aerated lung parenchyma: 51%, (b) combination of blood tests of lactic dehydrogenase, lymphocyte, and C-reactive protein: 63%, and (c) U-survival: 87%. Thus, the U-survival significantly outperformed clinical biomarkers in predicting the survival of COVID-19 patients, indicating that the U-radiomics vector of the U-survival model may provide a highly accurate prognostic biomarker for patients with COVID-19.

DOI： 10.1117/12.2581907

Scopus

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Image Registration Method for Temporal Subtraction Based on Salient Region Features 査読有り

Sato S., Lu H., Kim H., Murakami S., Ueno M., Terasawa T., Aoki T.

EAI/Springer Innovations in Communication and Computing 13 - 20 2021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）

While diagnosis imaging is an indispensable technology in medical field, it causes an increase in the burden to radiologists. In recent years, computer-aided diagnosis (CAD) system for supporting a radiologist has been developed to solve this problem. Temporal subtraction, which is one of CAD, is a technique to generate images emphasizing temporal changes in lesions and facilitates diagnosis of radiologists. To make a temporal subtraction image, image registration technique is required. In this chapter, we propose an image registration method for image registration of current image and previous image to generate temporal subtraction images in a short time. The proposed method consists of three steps: (i) segmentation of the region of interest (ROI) using position information of the spine based on anatomical information, (ii) using global image matching to select pairs of previous image and current image in which the same portion is depicted, and iii) final image matching based on salient region features (SRF). We performed our registration technique to the synthetic data and confirmed usefulness of the proposed method. The rotated synthesis image gives TP 100.0% and FP 12.16%. The synthesis image obtained by applying a Gaussian filter gives TP 70.40% and FP 0.00%. The synthesis image obtained by adding artificial pseudo-lesion region gives TP 99.45% and FP 17.89%. The synthesis image obtained by adding random noise of 5% gives TP 83.05% and FP 16.95%. Furthermore, radiologist conducted comparative experiments without and with temporal subtraction images created by proposed method. As a result, radiologist showed high reading performance by using temporal subtraction images.

DOI： 10.1007/978-3-030-46032-7_2

Scopus

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Extreme ROS Reality: A Representation Framework for Robots Using Image Dehazing and VR 査読有り

Ueda A., Lu H., Kim H.

EAI/Springer Innovations in Communication and Computing 21 - 32 2021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）

It is necessary to work in response to various situations when using robots at disaster areas. So, the complete automation remote control technique of the robot is essential. In recent years, the remote control technique of the robot is mainly focused on recognizing the working environment of the robot from the 2D monitor and performing the operation. In order to operate the robot correctly, a corresponding technique and sufficiently trained engineers are required. To this end, a new remote control technology that can operate more easily is required. Also, depending on the disaster environment that working at night or indoors is assumed, it becomes difficult to recognize the work environment correctly from the image by the sensor such as the camera due to insufficient light source. In this chapter, in order to solve these problems, as a development of new remote control technology, we propose the method of reproducing the working environment and the image improvement method under the low light source environment. In this chapter, we reproduce the working environment of remote areas by using virtual reality. In addition, we improve the image under low light source environment using haze removal algorithm. The work environment of the robot is acquired by RGBD sensor. Image improvement processing is performed on the obtained color image, and the image data is transmitted to a remote place where the operator is located via WebSocket. The experiments show that our method reproduces the working environment of the robot as point group to the virtual reality space and examine its usefulness.

DOI： 10.1007/978-3-030-46032-7_3

Scopus

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An Adaptable Feature Synthesis for Camouflage 査読有り

Li G., Yu Z., Chang J., Kim H.

EAI/Springer Innovations in Communication and Computing 81 - 91 2021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）

Military camouflage should be various in order to quickly adapt environmental changes of battlefield. The elusiveness is depressed if the constant camouflage patterns of the clothes and vehicles are used, as applying in the most of current military. In this paper, we propose a texture synthesis method from an image using convolutional neural networks, which has an effect on the camouflage generation on a 3D surface. We use the latest advances in style transitions in 2D images and map characteristic textures onto 3D surfaces according to geometric feature. This allows us to make an adaptive texture map of the 3D objects, even in the case of complex topologies.

DOI： 10.1007/978-3-030-46032-7_8

Scopus

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A generative flow-based model for volumetric data augmentation in 3D deep learning for computed tomographic colonography 査読有り

Uemura T., Näppi J.J., Ryu Y., Watari C., Kamiya T., Yoshida H.

International Journal of Computer Assisted Radiology and Surgery 16 ( 1 ) 81 - 89 2021年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

Purpose: Deep learning can be used for improving the performance of computer-aided detection (CADe) in various medical imaging tasks. However, in computed tomographic (CT) colonography, the performance is limited by the relatively small size and the variety of the available training datasets. Our purpose in this study was to develop and evaluate a flow-based generative model for performing 3D data augmentation of colorectal polyps for effective training of deep learning in CADe for CT colonography. Methods: We developed a 3D-convolutional neural network (3D CNN) based on a flow-based generative model (3D Glow) for generating synthetic volumes of interest (VOIs) that has characteristics similar to those of the VOIs of its training dataset. The 3D Glow was trained to generate synthetic VOIs of polyps by use of our clinical CT colonography case collection. The evaluation was performed by use of a human observer study with three observers and by use of a CADe-based polyp classification study with a 3D DenseNet. Results: The area-under-the-curve values of the receiver operating characteristic analysis of the three observers were not statistically significantly different in distinguishing between real polyps and synthetic polyps. When trained with data augmentation by 3D Glow, the 3D DenseNet yielded a statistically significantly higher polyp classification performance than when it was trained with alternative augmentation methods. Conclusion: The 3D Glow-generated synthetic polyps are visually indistinguishable from real colorectal polyps. Their application to data augmentation can substantially improve the performance of 3D CNNs in CADe for CT colonography. Thus, 3D Glow is a promising method for improving the performance of deep learning in CADe for CT colonography.

DOI： 10.1007/s11548-020-02275-z

Scopus

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Underwater image super-resolution using SRCNN 査読有り国際誌

Ooyama S., Lu H., Kamiya T., Serikawa S.

Proceedings of SPIE - The International Society for Optical Engineering 11884 2021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, energy minerals have become more important due to the rapid industrialization worldwide. Due to the rapid industrialization on a global scale, there is a shortage of mineral resources, and there are more opportunities to rely on alternative energy sources. Therefore, the exploration of marine resources, which are abundant in the ocean, is being promoted. However, it is dangerous and impractical for humans to dive and search for marine resources by hand. Therefore, it is possible to proceed with underwater exploration safely by having a robot do the work instead. Robots have been used as a mainstream search tool in the underwater environment due to the existence of various hazardous environmental conditions. However, there are several problems associated with robot control in underwater environments, one of which is poor visibility in the water. One of the problems is the poor visibility in the water. To improve the visibility in the water, we are trying to increase the resolution of underwater images by using super-resolution technology. In this paper, we conduct experiments using SRCNN, which is a basic super-resolution technique for underwater images. In addition, we investigate the effectiveness of "Mish", which has been attracting attention in recent years for its potential to surpass the performance of "ReLU", although "ReLU"is a typical activation function of neural networks, on SRCNN.

DOI： 10.1117/12.2603761

Scopus

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Shape restoration by shadow information and photometric stereo 査読有り国際誌

Kameda S., Lu H., Kamiya T., Serikawa S.

Proceedings of SPIE - The International Society for Optical Engineering 11884 2021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）

Virtual Reality (VR) systems are become popular in recent years, and the capture 3D objects from the real world have been studied. 3D objects have large and complex data. In this paper, we propose a novel method that use the shadow information and Photometric stereo with their surface from one point of view to recover the 3D shapes. The experimental results show that the proposed method performs well accuracy.

DOI： 10.1117/12.2604193

Scopus

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Image quality improvement using local adaptive neighborhood-based dark channel prior 査読有り国際誌

Onoyama T., Lu H., Soomro A.A., Mokhtar A.A., Kamiya T., Serikawa S.

Proceedings of SPIE - The International Society for Optical Engineering 11884 2021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-vehicle cameras and surveillance cameras are used in many situations in our daily lives. Visibility degradation in foggy environments is caused by the scattering of reflected light from real objects by minute water droplets or fog in the medium through which light passes. The degree of degradation depends on the density of suspended microparticles existing between the observed object and the observation point in the medium. In general, the farther the object is from the camera, the more it is affected by the fog. The purpose of image de-fogging is to improve the clarity of an object by removing the effects of fog in the image.

DOI： 10.1117/12.2603771

Scopus

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