研究者詳細 - 神谷　亨

論文 - 神谷　亨

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Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT 査読有り

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

Medical Image Analysis 73 2021年10月

記述言語：英語掲載種別：研究論文（学術雑誌）

Because of the rapid spread and wide range of the clinical manifestations of the coronavirus disease 2019 (COVID-19), fast and accurate estimation of the disease progression and mortality is vital for the management of the patients. Currently available image-based prognostic predictors for patients with COVID-19 are largely limited to semi-automated schemes with manually designed features and supervised learning, and the survival analysis is largely limited to logistic regression. We developed a weakly unsupervised conditional generative adversarial network, called pix2surv, which can be trained to estimate the time-to-event information for survival analysis directly from the chest computed tomography (CT) images of a patient. We show that the performance of pix2surv based on CT images significantly outperforms those of existing laboratory tests and image-based visual and quantitative predictors in estimating the disease progression and mortality of COVID-19 patients. Thus, pix2surv is a promising approach for performing image-based prognostic predictions.

DOI： 10.1016/j.media.2021.102159

Scopus

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Classification of respiratory sounds using improved convolutional recurrent neural network 査読有り

Asatani N., Kamiya T., Mabu S., Kido S.

Computers and Electrical Engineering 94 2021年09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）

Currently, auscultation using a stethoscope is performed for the diagnosis of respiratory diseases. Auscultation is a simple and non-invasive diagnostic method; however, the diagnostic results depend on the experience of the doctor, thereby rendering quantitative diagnosis difficult. Therefore, we herein propose a new automatic classification method based on deep learning algorithms for respiratory sounds to support the diagnosis of respiratory diseases. The proposed method comprises two stages. First, a spectrogram is generated by applying a short-time Fourier transform to the respiratory sound data. Subsequently, the obtained spectrogram is classified into normal and abnormal (three classes: crackle, wheeze, and both) respiratory sounds using an improved convolutional recurrent neural network. By classifying the respiratory sounds using the proposed method, the following results are obtained: sensitivity, 0.63; specificity,0.83; average score, 0.73; harmonic score, 0.72. Furthermore, the proposed method yields better accuracy compared with other methods.

DOI： 10.1016/j.compeleceng.2021.107367

Scopus

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Construction of a Hierarchical Feature Enhancement Network and Its Application in Fault Recognition 査読有り

Chen Z., Lu H., Tian S., Qiu J., Kamiya T., Serikawa S., Xu L.

IEEE Transactions on Industrial Informatics 17 ( 7 ) 4827 - 4836 2021年07月

記述言語：英語掲載種別：研究論文（学術雑誌）

Industrial Internet of Things (IIoT) provide significant support for observing and controlling industrial machinery. In this article, a novel hierarchical feature enhancement network (HFEN) is proposed by combining signal processing and representation learning. The signal processing block extracts features with definite physical significance. Then, the representability of the physical features is improved by connecting stacked denoising autoencoders and squeeze-and-excitation networks. A novel two-stream architecture is designed for HFEN to fuse two types of features. Consequently, HFEN can extract features that can be analyzed for physical significance and that are also representative in terms of recognizable patterns. The experimental results prove that the performance of HFEN is satisfactory in terms of accuracy and efficiency when compared to other methods. Finally, this article also aims to demonstrate the potential of a new pairing that fuses the model-and data-driven strategies for IIoT.

DOI： 10.1109/TII.2020.3021688

Scopus

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Computer-aided prediction of COVID-19 progression using unsupervised deep learning of chest CT images 査読有り国際誌

Uemura T, Näppi JJ, Watari C, Hironaka T, Kamiya T, Yoshida H.

Computer Assisted Radiology and Surgery 2021年06月

HPSSを用いた呼吸音の自動分類査読有り

丸橋,　浅谷, 陸, 神谷, 間普, 木戸

医用画像情報学会雑誌 38 ( 2 ) 95 - 10 2021年06月

Visual information processing for deep-sea visual monitoring system 査読有り国際誌

Ma, Li, Li, Tian, Wang, Kim, Serikawa

Cognitive Robotics ( Cognitive Robotics ) 1 3 - 11 2021年06月

記述言語：英語掲載種別：研究論文（学術雑誌）

Due to the rising demand for minerals and metals, various deep-sea mining systems have been developed for the detection of mines and mine-like objects on the seabed. However, many of them contain some issues due to the diffusion of dangerous substances and radioactive substances in water. Therefore, efficient and accurate visual monitoring is expected by introducing artificial intelligence. Most recent deep-sea mining machines have little intelligence in visual monitoring systems. Intelligent robotics, e.g., deep learning-based edge computing for deep-sea visual monitoring systems, have not yet been established. In this paper, we propose the concept of a learning-based deep-sea visual monitoring system and use testbeds to show the efficiency of the proposed system. For example, to sense the underwater environment in real time, a large quantity of observation data, including captured images, must be transmitted from the seafloor to the ship, but large-capacity underwater communication is difficult. In this paper, we propose using deep compressed learning for real-time communication. In addition, we propose the gradient generation adversarial network (GGAN) to recover the heavily destroyed underwater images. In the application layer, wavelet-aware superresolution is used to show high-resolution images. Therefore, the development of an intelligent remote control deep-sea mining system with good convenience using deep learning applicable to deep-sea mining is expected.

DOI： 10.1016/j.cogr.2020.12.002

Scopus

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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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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

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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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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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

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

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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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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