研究者詳細 - 森　健策

論文 - 森　健策

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Improved method for COVID-19 Classification of Complex-Architecture CNN from Chest CT volumes using Orthogonal Ensemble Networks 招待有り査読有り

Toda R., Oda M., Hayashi Y., Otake Y., Hashimoto M., Akashi T., Aoki S., Mori K.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE 12465 巻 2023年

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）出版者・発行元：Progress in Biomedical Optics and Imaging - Proceedings of SPIE

This paper introduces the improved method for the COVID-19 classification based on computed tomography (CT) volumes using a combination of a complex-architecture convolutional neural network (CNN) and orthogonal ensemble networks (OEN). The novel coronavirus disease reported in 2019 (COVID-19) is still spreading worldwide. Early and accurate diagnosis of COVID-19 is required in such a situation, and the CT scan is an essential examination. Various computer-aided diagnosis (CAD) methods have been developed to assist and accelerate doctors' diagnoses. Although one of the effective methods is ensemble learning, existing methods combine some major models which do not specialize in COVID-19. In this study, we attempted to improve the performance of a CNN for the COVID-19 classification based on chest CT volumes. The CNN model specializes in feature extraction from anisotropic chest CT volumes. We adopt the OEN, an ensemble learning method considering inter-model diversity, to boost its feature extraction ability. For the experiment, We used chest CT volumes of 1283 cases acquired in multiple medical institutions in Japan. The classification result on 257 test cases indicated that the combination could improve the classification performance.

DOI： 10.1117/12.2653792

Scopus

Classification of COVID-19 cases from chest CT volumes using hybrid model of 3D CNN and 3D MLP-Mixer 招待有り査読有り

Oda M., Zheng T., Hayashi Y., Otake Y., Hashimoto M., Akashi T., Aoki S., Mori K.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE 12465 巻 2023年

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）出版者・発行元：Progress in Biomedical Optics and Imaging - Proceedings of SPIE

This paper proposes an automated classification method of COVID-19 chest CT volumes using improved 3D MLP-Mixer. Novel coronavirus disease 2019 (COVID-19) spreads over the world, causing a large number of infected patients and deaths. Sudden increase in the number of COVID-19 patients causes a manpower shortage in medical institutions. Computer-aided diagnosis (CAD) system provides quick and quantitative diagnosis results. CAD system for COVID-19 enables efficient diagnosis workflow and contributes to reduce such manpower shortage. In image-based diagnosis of viral pneumonia cases including COVID-19, both local and global image features are important because viral pneumonia cause many ground glass opacities and consolidations in large areas in the lung. This paper proposes an automated classification method of chest CT volumes for COVID-19 diagnosis assistance. MLP-Mixer is a recent method of image classification using Vision Transformer-like architecture. It performs classification using both local and global image features. To classify 3D CT volumes, we developed a hybrid classification model that consists of both a 3D convolutional neural network (CNN) and a 3D version of the MLP-Mixer. Classification accuracy of the proposed method was evaluated using a dataset that contains 1205 CT volumes and obtained 79.5% of classification accuracy. The accuracy was higher than that of conventional 3D CNN models consists of 3D CNN layers and simple MLP layers.

DOI： 10.1117/12.2654706

Scopus

Automated Detection of the Thoracic Ossification of the Posterior Longitudinal Ligament Using Deep Learning and Plain Radiographs. 査読有り Open Access

Ito S, Nakashima H, Segi N, Ouchida J, Oda M, Yamauchi I, Oishi R, Miyairi Y, Mori K, Imagama S

BioMed research international 2023 巻頁： 8495937 2023年

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）出版者・発行元：Biomed Research International

Ossification of the ligaments progresses slowly in the initial stages, and most patients are unaware of the disease until obvious myelopathy symptoms appear. Consequently, treatment and clinical outcomes are not satisfactory. This study is aimed at developing an automated system for the detection of the thoracic ossification of the posterior longitudinal ligament (OPLL) using deep learning and plain radiography. We retrospectively reviewed the data of 146 patients with thoracic OPLL and 150 control cases without thoracic OPLL. Plain lateral thoracic radiographs were used for object detection, training, and validation. Thereafter, an object detection system was developed, and its accuracy was calculated. The performance of the proposed system was compared with that of two spine surgeons. The accuracy of the proposed object detection model based on plain lateral thoracic radiographs was 83.4%, whereas the accuracies of spine surgeons 1 and 2 were 80.4% and 77.4%, respectively. Our findings indicate that our automated system, which uses a deep learning-based method based on plain radiographs, can accurately detect thoracic OPLL. This system has the potential to improve the diagnostic accuracy of thoracic OPLL.

DOI： 10.1155/2023/8495937

Open Access

Scopus

PubMed

AIによる腹腔鏡下手術支援招待有り査読有り

林雄一郎, 森健策

Ｍｅｄｉｃａｌ　Ｉｍａｇｉｎｇ　Ｔｅｃｈｎｏｌｏｇｙ 40 巻 ( 4 ) 頁： 164 - 169 2022年9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）出版者・発行元：日本医用画像工学会

<p>腹腔鏡下手術は，外科手術の一つの方法として広く行われるようになった．腹腔鏡下手術は高度な技術を要するため，コンピューターを用いて外科医を支援するコンピューター支援外科の研究が行われている．これらは，手術ナビゲーションとして一般的にはとらえられている．近年のAI技術の発展に伴い，腹腔鏡下手術支援のために深層学習を利用して腹腔鏡から得られる手術映像を解析する研究も行われるようになってきた．本稿では，筆者らの研究グループで取り組んでいるAIを用いた腹腔鏡映像の解析に関する研究を紹介する．</p>

DOI： 10.11409/mit.40.164

CiNii Research

AI for VR 招待有り査読有り

森健策

日本VR医学会学術大会プログラム・抄録集 2022 巻 ( 0 ) 頁： 9 - 9 2022年8月

BONE MARROW EDEMA SCORE IN HAND X-RAY FILM BY AI DEEP LEARNING ASSOCIATE WITH MRI BONE EDEMA IN RHEUMATOID ARTHRITIS. 招待有り査読有り Open Access

Katayama, K; Pan, D; Oda, M; Okubo, T; Mori, K

ANNALS OF THE RHEUMATIC DISEASES 81 巻頁： 1773 - 1774 2022年6月

30年間の医用画像研究経験を振り返り未来を考える招待有り Open Access

森健策

情報・システムソサイエティ誌 27 巻 ( 1 ) 頁： 16 - 17 2022年5月

Depth Estimation from Single-shot Monocular Endoscope Image Using Image Domain Adaptation And Edge-Aware Depth Estimation 査読有り Open Access

Masahiro Oda, Hayato Itoh, Kiyohito Tanaka, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 10 巻 ( 3 ) 頁： 266 - 273 2022年5月

Uncertainty meets 3D-spatial feature in colonoscopic polyp-size determination 招待有り査読有り

Hayato Itoh, Masahiro Oda, Kai Jiang, Yuichi Mori, Masashi Misawa, Shin-Ei Kudo, Kenichiro Imai, Sayo Ito, Kinichi Hotta, Kensaku Mori

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 10 巻 ( 3 ) 頁： 289 - 298 2022年5月

Spatially variant biases considered self-supervised depth estimation based on laparoscopic videos 査読有り

Wenda Li, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 10 巻 ( 3 ) 頁： 274 - 282 2022年5月

Artificial Intelligence-Based Total Mesorectal Excision Plane Navigation in Laparoscopic Colorectal Surgery

Igaki, T; Kitaguchi, D; Kojima, S; Hasegawa, H; Takeshita, N; Mori, K; Kinugasa, Y; Ito, M

DISEASES OF THE COLON & RECTUM 65 巻 ( 5 ) 頁： E329 - E333 2022年5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）出版者・発行元：Diseases of the Colon and Rectum

BACKGROUND: Total mesorectal excision is the standard surgical procedure for rectal cancer because it is associated with low local recurrence rates. To the best of our knowledge, this is the first study to use an image-guided navigation system with total mesorectal excision. IMPACT OF INNOVATION: The impact of innovation is the development of a deep learning-based image-guided navigation system for areolar tissue in the total mesorectal excision plane. Such a system might be helpful to surgeons because areolar tissue can be used as a landmark for the appropriate dissection plane. TECHNOLOGY, MATERIALS, AND METHODS: This was a single-center experimental feasibility study involving 32 randomly selected patients who had undergone laparoscopic left-sided colorectal resection between 2015 and 2019. Deep learning-based semantic segmentation of areolar tissue in the total mesorectal excision plane was performed. Intraoperative images capturing the total mesorectal excision scene extracted from left colorectal laparoscopic resection videos were used as training data for the deep learning model. Six hundred annotation images were created from 32 videos, with 528 images in the training and 72 images in the test data sets. The experimental feasibility study was conducted at the Department of Colorectal Surgery, National Cancer Center Hospital East, Chiba, Japan. Dice coefficient was used to evaluate semantic segmentation accuracy for areolar tissue. PRELIMINARY RESULTS: The developed semantic segmentation model helped locate and highlight the areolar tissue area in the total mesorectal excision plane. The accuracy and generalization performance of deep learning models depend mainly on the quantity and quality of the training data. This study had only 600 images; thus, more images for training are necessary to improve the recognition accuracy. CONCLUSION AND FUTURE DIRECTIONS: We successfully developed a total mesorectal excision plane image-guided navigation system based on an areolar tissue segmentation approach with high accuracy. This may aid surgeons in recognizing the total mesorectal excision plane for dissection.

DOI： 10.1097/DCR.0000000000002393

SR-CycleGAN: super-resolution of clinical CT to micro-CT level with multi-modality super-resolution loss 査読有り Open Access

Tong Zheng, Hirohisa Oda, Yuichiro Hayashi, Takayasu Moriya, Shota Nakamura, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Masahiro Oda, Kensaku Mori

Journal of Medical Imaging 9 巻 ( 2 ) 頁： 024003-1 - 28 2022年4月

Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study 招待有り査読有り

Yasuharu Maeda, Shin-ei Kudo, Noriyuki Ogata, Masashi Misawa, Marietta Iacucci, Mayumi Homma, Tetsuo Nemoto, Kazumi Takishima, Kentaro Mochida, Hideyuki Miyachi, Toshiyuki Baba, Kensaku Mori, Kazuo Ohtsuka, Yuichi Mori

Gastrointestinal Endoscopy 2022年4月

Artificial Intelligence for Computer Vision in Surgery <i>A Call for Developing Reporting Guidelines</i> 招待有り査読有り Open Access

Kitaguchi, D; Watanabe, Y; Madani, A; Hashimoto, DA; Meireles, OR; Takeshita, N; Mori, K; Ito, M

ANNALS OF SURGERY 275 巻 ( 4 ) 頁： E609 - E611 2022年4月

A cascaded fully convolutional network framework for dilated pancreatic duct segmentation 招待有り査読有り

Shen, C; Roth, HR; Hayashi, Y; Oda, M; Miyamoto, T; Sato, G; Mori, K

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 17 巻 ( 2 ) 頁： 343 - 354 2022年2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）出版者・発行元：International Journal of Computer Assisted Radiology and Surgery

Purpose: Pancreatic duct dilation can be considered an early sign of pancreatic ductal adenocarcinoma (PDAC). However, there is little existing research focused on dilated pancreatic duct segmentation as a potential screening tool for people without PDAC. Dilated pancreatic duct segmentation is difficult due to the lack of readily available labeled data and strong voxel imbalance between the pancreatic duct region and other regions. To overcome these challenges, we propose a two-step approach for dilated pancreatic duct segmentation from abdominal computed tomography (CT) volumes using fully convolutional networks (FCNs). Methods: Our framework segments the pancreatic duct in a cascaded manner. The pancreatic duct occupies a tiny portion of abdominal CT volumes. Therefore, to concentrate on the pancreas regions, we use a public pancreas dataset to train an FCN to generate an ROI covering the pancreas and use a 3D U-Net-like FCN for coarse pancreas segmentation. To further improve the dilated pancreatic duct segmentation, we deploy a skip connection on each corresponding resolution level and an attention mechanism in the bottleneck layer. Moreover, we introduce a combined loss function based on Dice loss and Focal loss. Random data augmentation is adopted throughout the experiments to improve the generalizability of the model. Results: We manually created a dilated pancreatic duct dataset with semi-automated annotation tools. Experimental results showed that our proposed framework is practical for dilated pancreatic duct segmentation. The average Dice score and sensitivity were 49.9% and 51.9%, respectively. These results show the potential of our approach as a clinical screening tool. Conclusions: We investigate an automated framework for dilated pancreatic duct segmentation. The cascade strategy effectively improved the segmentation performance of the pancreatic duct. Our modifications to the FCNs together with random data augmentation and the proposed combined loss function facilitate automated segmentation.

DOI： 10.1007/s11548-021-02530-x

Web of Science

Scopus

PubMed

impact of the clinical use of artificial intelligence-assisted neoplasia detection for colonoscopy: a large-scale prospective, propensity score-matched study (with video) 査読有り

Misaki Ishiyama, Shin-ei Kudo, Masashi Misawa, Yuichi Mori, Yasuharu Maeda, Katsuro Ichimasa, Toyoki Kudo, Takemasa Hayashi, Kunihiko Wakamura, Hideyuki Miyachi, Fumio Ishida, Hayato Itoh, Masahiro Oda, Kensaku Mori

95 巻 ( 1 ) 頁： 155 - 163 2022年1月

Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning 招待有り査読有り

Shen, C; Wang, PC; Yang, D; Xu, DG; Oda, M; Chen, PT; Liu, KL; Liao, WC; Fuh, CS; Mori, K; Wang, WC; Roth, HR

DISTRIBUTED, COLLABORATIVE, AND FEDERATED LEARNING, AND AFFORDABLE AI AND HEALTHCARE FOR RESOURCE DIVERSE GLOBAL HEALTH, DECAF 2022, FAIR 2022 13573 巻頁： 58 - 67 2022年

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）出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Segmentation studies in medical image analysis are always associated with a particular task scenario. However, building datasets to train models to segment multiple types of organs and pathologies is challenging. For example, a dataset annotated for the pancreas and pancreatic tumors will result in a model that cannot segment other organs, like the liver and spleen, visible in the same abdominal computed tomography image. The lack of a well-annotated dataset is one limitation resulting in a lack of universal segmentation models. Federated learning (FL) is ideally suited for addressing this issue in the real-world context. In this work, we show that each medical center can use training data for distinct tasks to collaboratively build more generalizable segmentation models for multiple segmentation tasks without the requirement to centralize datasets in one place. The main challenge of this research is the heterogeneity of training data from various institutions and segmentation tasks. In this paper, we propose a multi-task segmentation framework using FL to learn segmentation models using several independent datasets with different annotations of organs or tumors. We include experiments on four publicly available single-task datasets, including MSD liver (w/ tumor), MSD spleen, MSD pancreas (w/ tumor), and KITS19. Experimental results on an external validation set to highlight the advantages of employing FL in multi-task organ and tumor segmentation.

DOI： 10.1007/978-3-031-18523-6_6

Web of Science

Scopus

Automated classification method of COVID-19 cases from chest CT volumes using 2D and 3D hybrid CNN for anisotropic volumes 招待有り査読有り

Oda, M; Zheng, T; Hayashi, Y; Otake, Y; Hashimoto, M; Akashi, T; Aoki, S; Mori, K

MEDICAL IMAGING 2022: COMPUTER-AIDED DIAGNOSIS 12033 巻 2022年

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）出版者・発行元：Progress in Biomedical Optics and Imaging Proceedings of SPIE

This paper proposes an automated classification method of chest CT volumes based on likelihood of COVID-19 cases. Novel coronavirus disease 2019 (COVID-19) spreads over the world, causing a large number of infected patients and deaths. Sudden increase in the number of COVID-19 patients causes a manpower shortage in medical institutions. Computer-aided diagnosis (CAD) system provides quick and quantitative diagnosis results. CAD system for COVID-19 enables efficient diagnosis workflow and contributes to reduce such manpower shortage. This paper proposes an automated classification method of chest CT volumes for COVID-19 diagnosis assistance. We propose a COVID-19 classification convolutional neural network (CNN) that has a 2D/3D hybrid feature extraction flows. The 2D/3D hybrid feature extraction flows are designed to effectively extract image features from anisotropic volumes such as chest CT volumes for diagnosis. The flows extract image features on three mutually perpendicular planes in CT volumes and then combine the features to perform classification. Classification accuracy of the proposed method was evaluated using a dataset that contains 1288 CT volumes. An averaged classification accuracy was 83.3%. The accuracy was higher than that of a classification CNN which does not have 2D and 3D hybrid feature extraction flows.

DOI： 10.1117/12.2613317

Web of Science

Scopus

Depth-based branching level estimation for bronchoscopic navigation 招待有り査読有り

Wang, C; Hayashi, Y; Oda, M; Kitasaka, T; Takabatake, H; Mori, M; Honma, H; Natori, H; Mori, K

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 16 巻 ( 10 ) 頁： 1795 - 1804 2021年10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）出版者・発行元：International Journal of Computer Assisted Radiology and Surgery

Purpose: Bronchoscopists rely on navigation systems during bronchoscopy to reduce the risk of getting lost in the complex bronchial tree-like structure and the homogeneous bronchus lumens. We propose a patient-specific branching level estimation method for bronchoscopic navigation because it is vital to identify the branches being examined in the bronchus tree during examination. Methods: We estimate the branching level by integrating the changes in the number of bronchial orifices and the camera motions among the frames. We extract the bronchial orifice regions from a depth image, which is generated using a cycle generative adversarial network (CycleGAN) from real bronchoscopic images. We calculate the number of orifice regions using the vertical and horizontal projection profiles of the depth images and obtain the camera-moving direction using the feature point-based camera motion estimation. The changes in the number of bronchial orifices are combined with the camera-moving direction to estimate the branching level. Results: We used three in vivo and one phantom case to train the CycleGAN model and four in vivo cases to validate the proposed method. We manually created the ground truth of the branching level. The experimental results showed that the proposed method can estimate the branching level with an average accuracy of 87.6%. The processing time per frame was about 61 ms. Conclusion: Experimental results show that it is feasible to estimate the branching level using the number of bronchial orifices and camera-motion estimation from real bronchoscopic images.

DOI： 10.1007/s11548-021-02460-8

Web of Science

Scopus

PubMed

Artificial intelligence and computer-aided diagnosis for colonoscopy: where do we stand now? 招待有り査読有り Open Access

Kudo Shin-Ei, Mori Yuichi, Abdel-aal Usama M., Misawa Masashi, Itoh Hayato, Oda Masahiro, Mori Kensaku

TRANSLATIONAL GASTROENTEROLOGY AND HEPATOLOGY 6 巻頁： 64 2021年10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）出版者・発行元：Translational Gastroenterology and Hepatology

Computer-aided diagnosis (CAD) for colonoscopy with use of artificial intelligence (AI) is catching increased attention of endoscopists. CAD allows automated detection and pathological prediction, namely optical biopsy, of colorectal polyps during real-time endoscopy, which help endoscopists avoid missing and/or misdiagnosing colorectal lesions. With the increased number of publications in this field and emergence of the AI medical device that have already secured regulatory approval, CAD in colonoscopy is now being implemented into clinical practice. On the other side, drawbacks and weak points of CAD in colonoscopy have not been thoroughly discussed. In this review, we provide an overview of CAD for optical biopsy of colorectal lesions with a particular focus on its clinical applications and limitations.

DOI： 10.21037/tgh.2019.12.14

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