研究者詳細 - 森　健策

論文 - 森　健策

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Deep learning system for automatic detection of bladder tumors in cystoscopic images 招待有り査読有り

Mutaguchi J., Oda M., Ueda S., Kinoshita F., Naganuma H., Matsumoto T., Lee K., Monji K., Kashiwagi K., Takeuchi A., Shiota M., Inokuchi J., Mori K., Eto M.

EUROPEAN UROLOGY 79 巻頁： S1022 - S1023 2021年6月

COVID-19診断支援AI開発における名古屋大学の取り組み招待有り査読有り Open Access

小田昌宏, 鄭通, 林雄一郎, 森健策

Ｍｅｄｉｃａｌ　Ｉｍａｇｉｎｇ　Ｔｅｃｈｎｏｌｏｇｙ 39 巻 ( 1 ) 頁： 13 - 19 2021年1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>本稿では，名古屋大学におけるCT像からのCOVID-19診断支援AI開発に対する取り組みを紹介する．新型コロナウイルス感染症（COVID-19）が急速に世界に広まり，多くの感染者と死亡者を生じている．このように多数の患者を迅速に診断する必要がある状況では，AIによる診断支援が有効と考える．われわれは患者のCT像から画像所見に基づくCOVID-19典型度を自動判別するAI開発を行った．AIによる自動判別に必要となる3つの要素技術として肺野セグメンテーション，肺野領域クラスタリング，COVID-19典型度推定を開発し，CT像からの自動判別AIの処理フロー全体を構築した．本AIを開発する上で，多数の医療機関で撮影された膨大な画像を格納した医療画像ビッグデータクラウド基盤を活用し，実際のCOVID-19患者のCT像に対して高い精度での自動判別が可能であることを確認した．</p>

DOI： 10.11409/mit.39.13

Open Access

CiNii Research

Automated Detection of Spinal Schwannomas Utilizing Deep Learning Based on Object Detection From Magnetic Resonance Imaging 招待有り国際共著

Ito, S; Ando, K; Kobayashi, K; Nakashima, H; Oda, M; Machino, M; Kanbara, S; Inoue, T; Yamaguchi, H; Koshimizu, H; Mori, K; Ishiguro, N; Imagama, S

SPINE 46 巻 ( 2 ) 頁： 95 - 100 2021年1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：Spine

Study Design.A retrospective analysis of magnetic resonance imaging (MRI) was conducted.Objective.This study aims to develop an automated system for the detection of spinal schwannoma, by employing deep learning based on object detection from MRI. The performance of the proposed system was verified to compare the performances of spine surgeons.Summary of Background Data.Several MRI scans were conducted for the diagnoses of patients suspected to suffer from spinal diseases. Typically, spinal diseases do not involve tumors on the spinal cord, although a few tumors may exist at the unexpectable level or without symptom by chance. It is difficult to recognize these tumors; in some cases, these tumors may be overlooked. Hence, a deep learning approach based on object detection can minimize the probability of overlooking these tumors.Methods.Data from 50 patients with spinal schwannoma who had undergone MRI were retrospectively reviewed. Sagittal T1- and T2-weighted magnetic resonance imaging (T1WI and T2WI) were used in the object detection training and for validation. You Only Look Once version3 was used to develop the object detection system, and its accuracy was calculated. The performance of the proposed system was compared to that of two doctors.Results.The accuracies of the proposed object detection based on T1W1, T2W1, and both T1W1 and T2W1 were 80.3%, 91.0%, and 93.5%, respectively. The accuracies of the doctors were 90.2% and 89.3%.Conclusion.Automated object detection of spinal schwannoma was achieved. The proposed system yielded a high accuracy that was comparable to that of the doctors.Level of Evidence: 4.

DOI： 10.1097/BRS.0000000000003749

Depth estimation from single-shot monocular endoscope image using image domain adaptation and edge-aware depth estimation 招待有り査読有り Open Access

Oda M., Itoh H., Tanaka K., Takabatake H., Mori M., Natori H., Mori K.

Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization 2021年

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：Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

We propose a depth estimation method from a single-shot monocular endoscopic image using Lambertian surface translation by domain adaptation and depth estimation using multi-scale edge loss. We employ a two-step estimation process including Lambertian surface translation from unpaired data and depth estimation. The texture and specular reflection on the surface of an organ reduce the accuracy of depth estimations. We apply Lambertian surface translation to an endoscopic image to remove these texture and reflections. Then, we estimate the depth by using a fully convolutional network (FCN). During the training of the FCN, improvement of the object edge similarity between an estimated image and a ground truth depth image is important for getting better results. We introduced a muti-scale edge loss function to improve the accuracy of depth estimation. We quantitatively evaluated the proposed method using real colonoscopic images. The estimated depth values were proportional to the real depth values. Furthermore, we applied the estimated depth images to automated anatomical location identification of colonoscopic images using a convolutional neural network. The identification accuracy of the network improved from 69.2% to 74.1% by using the estimated depth images.

DOI： 10.1080/21681163.2021.2012835

Open Access

Scopus

Context encoder guided self-supervised siamese depth estimation based on stereo laparoscopic images 招待有り査読有り

Li W., Hayashi Y., Oda M., Kitasaka T., Misawa K., Mori K.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE 11598 巻 2021年

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：Progress in Biomedical Optics and Imaging - Proceedings of SPIE

This paper proposes a novel self-supervised depth estimation method guided by a context encoder. Depth estimation from stereo laparoscopic images is essential to robotic surgical navigation systems and robotic surgical platform. Recent work has shown that depth estimation of stereo image pairs can be formulated as a self- supervised learning task without ground-truth. However, most architectures based on convolutional neural lead to lose some spatial information because of the consecutive pooling and convolution operations. In order to tackle this problem, we add a contextual encoding module to the previous method. The context encoder module is formed by dense atrous convolution block and spatial pyramid pooling block that are used to extract and merge features on different scales. Also, we add the edge-awared smoothness for predicted disparity maps. In addition, we output multi-scale disparity predictions and corresponding image reconstruction for loss calculating. In the experiments, we showed that the proposed method has about 7.79% improvement in SSIM and about 17.76% improvement in PSNR for stereo image pairs compared with previous method. Also, the disparity maps and reconstructed images given by the proposed method have significant enhancements compared with the previous method.

DOI： 10.1117/12.2582348

Scopus

Bronchial orifice segmentation on bronchoscopic video frames based on generative adversarial depth estimation 招待有り査読有り

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

Progress in Biomedical Optics and Imaging - Proceedings of SPIE 11598 巻頁： 115980N-1 - 7 2021年

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：Progress in Biomedical Optics and Imaging - Proceedings of SPIE

This paper describes a bronchial orifice (BO) segmentation method on real bronchoscopic video frames by using depth images. The BO is one of the anatomical characteristics in the bronchus, which is critical in clinical applications such as bronchus scene description and navigation path generation. Previous work used image appearance and the gradation of the real bronchoscopic image to segment orifice region, which behaved poorly in complex scenes including bubble or changes in illumination. To obtain a better segmentation result of BO even in the complex scenes, we propose a BO segmentation method using the distance between the bronchoscope camera and the bronchus lumen, which is represented by a depth image. Since the depth image is unavailable due to devices limitation, we use an image-to-image domain translation network named cycle generative adversarial network (CycleGAN) to estimate depth images from real bronchoscopic images. The BO regions are considered as the regions whose distances are larger than a distance threshold. We decide the distance threshold according to the depth images' projection profiles. Experimental results showed that the proposed method can find BO regions in the real bronchoscopic videos in real-time. We manually labeled BO regions as ground truth to evaluate the proposed method. The average Dice score of the proposed method was 77.0 %.

DOI： 10.1117/12.2582341

Scopus

COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty 招待有り査読有り Open Access

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

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12969 LNCS 巻頁： 88 - 97 2021年

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：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

This paper proposes a segmentation method of infection regions in the lung from CT volumes of COVID-19 patients. COVID-19 spread worldwide, causing many infected patients and deaths. CT image-based diagnosis of COVID-19 can provide quick and accurate diagnosis results. An automated segmentation method of infection regions in the lung provides a quantitative criterion for diagnosis. Previous methods employ whole 2D image or 3D volume-based processes. Infection regions have a considerable variation in their sizes. Such processes easily miss small infection regions. Patch-based process is effective for segmenting small targets. However, selecting the appropriate patch size is difficult in infection region segmentation. We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions. The receptive field sizes can be defined as the patch size and the resolution of volumes where patches are clipped from. This paper proposes an infection segmentation network (ISNet) that performs patch-based segmentation and a scale uncertainty-aware prediction aggregation method that refines the segmentation result. We design ISNet to segment infection regions that have various intensity values. ISNet has multiple encoding paths to process patch volumes normalized by multiple intensity ranges. We collect prediction results generated by ISNets having various receptive field sizes. Scale uncertainty among the prediction results is extracted by the prediction aggregation method. We use an aggregation FCN to generate a refined segmentation result considering scale uncertainty among the predictions. In our experiments using 199 chest CT volumes of COVID-19 cases, the prediction aggregation method improved the dice similarity score from 47.6% to 62.1%.

DOI： 10.1007/978-3-030-90874-4_9

Scopus

Current status and future perspective on artificial intelligence for lower endoscopy 招待有り査読有り Open Access

Misawa M., Kudo S.E., Mori Y., Maeda Y., Ogawa Y., Ichimasa K., Kudo T., Wakamura K., Hayashi T., Miyachi H., Baba T., Ishida F., Itoh H., Oda M., Mori K.

Gastroenterological Endoscopy 63 巻 ( 7 ) 頁： 1402 - 1416 2021年

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：Gastroenterological Endoscopy

The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for nonexperts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed）which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.

DOI： 10.11280/gee.63.1402

Scopus

CiNii Research

1．機械学習によるCOVID-19症例CT画像の診断支援招待有り Open Access

森健策

映像情報メディア学会誌 75 巻 ( 3 ) 頁： 325 - 329 2021年

Clinical and Genetic Characteristics of 18 Patients from 13 Japanese Families with CRX-associated retinal disorder: Identification of Genotype-phenotype Association 招待有り査読有り Open Access

Fujinami-Yokokawa Y., Fujinami K., Kuniyoshi K., Hayashi T., Ueno S., Mizota A., Shinoda K., Arno G., Pontikos N., Yang L., Liu X., Sakuramoto H., Katagiri S., Mizobuchi K., Kominami T., Terasaki H., Nakamura N., Kameya S., Yoshitake K., Miyake Y., Kurihara T., Tsubota K., Miyata H., Iwata T., Tsunoda K., Nishimura T., Hayashizaki Y., Kondo M., Shimozawa N., Horiguchi M., Yamamoto S., Kuze M., Naoi N., Machida S., Shimada Y., Nakamura M., Fujikado T., Hotta Y., Takahashi M., Mochizuki K., Murakami A., Kondo H., Ishida S., Nakazawa M., Hatase T., Matsunaga T., Maeda A., Noda K., Tanikawa A., Yamamoto S., Yamamoto H., Araie M., Aihara M., Nakazawa T., Sekiryu T., Kashiwagi K., Kosaki K., Piero C., Fukuchi T., Hayashi A., Hosono K., Mori K., Tanaka K., Furuya K., Suzuki K., Kohata R., Yanagi Y., Minegishi Y., Iejima D., Suga A., Rossmiller B.P., Pan Y., Oshima T., Nakayama M., Teruyama Y., Yamamoto M., Minematsu N., Sanbe H., Mori D., Kijima Y., Mawatari G., Kurata K., Yamada N., Itoh M., Kawaji H., Murakawa Y.

Scientific Reports 10 巻 ( 1 ) 2020年12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：Scientific Reports

Inherited retinal disorder (IRD) is a leading cause of blindness, and CRX is one of a number of genes reported to harbour autosomal dominant (AD) and recessive (AR) causative variants. Eighteen patients from 13 families with CRX-associated retinal disorder (CRX-RD) were identified from 730 Japanese families with IRD. Ophthalmological examinations and phenotype subgroup classification were performed. The median age of onset/latest examination was 45.0/62.5 years (range, 15–77/25–94). The median visual acuity in the right/left eye was 0.52/0.40 (range, −0.08–2.00/−0.18–1.70) logarithm of the minimum angle of resolution (LogMAR) units. There was one family with macular dystrophy, nine with cone-rod dystrophy (CORD), and three with retinitis pigmentosa. In silico analysis of CRX variants was conducted for genotype subgroup classification based on inheritance and the presence of truncating variants. Eight pathogenic CRX variants were identified, including three novel heterozygous variants (p.R43H, p.P145Lfs*42, and p.P197Afs*22). A trend of a genotype-phenotype association was revealed between the phenotype and genotype subgroups. A considerably high proportion of CRX-RD in ADCORD was determined in the Japanese cohort (39.1%), often showing the mild phenotype (CORD) with late-onset disease (sixth decade). Frequently found heterozygous missense variants located within the homeodomain underlie this mild phenotype. This large cohort study delineates the disease spectrum of CRX-RD in the Japanese population.

DOI： 10.1038/s41598-020-65737-z

Open Access

Scopus

Clinical characteristics in patients with ossification of the posterior longitudinal ligament: A prospective multi-institutional cross-sectional study 招待有り査読有り Open Access

Hirai T., Yoshii T., Ushio S., Mori K., Maki S., Katsumi K., Nagoshi N., Takeuchi K., Furuya T., Watanabe K., Nishida N., Watanabe K., Kaito T., Kato S., Nagashima K., Koda M., Ito K., Imagama S., Matsuoka Y., Wada K., Kimura A., Ohba T., Katoh H., Matsuyama Y., Ozawa H., Haro H., Takeshita K., Watanabe M., Matsumoto M., Nakamura M., Yamazaki M., Okawa A., Kawaguchi Y.

Scientific Reports 10 巻 ( 1 ) 2020年12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：Scientific Reports

Ossification of the posterior longitudinal ligament (OPLL) can occur throughout the entire spine and can sometimes lead to spinal disorder. Although patients with OPLL sometimes develop physical limitations because of pain, the characteristics of pain and effects on activities of daily living (ADL) have not been precisely evaluated in OPLL patients. Therefore, we conducted a multi-center prospective study to assess whether the symptoms of cervical OPLL are different from those of cervical spondylosis (CS). A total of 263 patients with a diagnosis of cervical OPLL and 50 patients with a diagnosis of CS were enrolled and provided self-reported outcomes, including responses to the Japanese Orthopaedic Association (JOA) Cervical Myelopathy Evaluation Questionnaire (JOACMEQ), JOA Back Pain Evaluation Questionnaire (JOABPEQ), visual analog scale (VAS), and SF-36 scores. The severity of myelopathy was significantly correlated with each domain of the JOACMEQ and JOABPEQ. There was a negative correlation between the VAS score for each domain and the JOA score. There were significantly positive correlations between the JOA score and the Mental Health, Bodily Pain, Physical Functioning, Role Emotional, and Role Physical domains of the SF-36. One-to-one matching resulted in 50 pairs of patients with OPLL and CS. Although there was no significant between-group difference in scores in any of the domains of the JOACMEQ or JOABPEQ, the VAS scores for pain or numbness in the buttocks or limbs were significantly higher in the CS group; however, there was no marked difference in low back pain, chest tightness, or numbness below the chest between the two study groups. The scores for the Role Physical and Body Pain domains of the SF-36 were significantly higher in the OPLL group than in the CS group, and the mean scores for the other domains was similar between the two groups. The results of this study revealed that patients with OPLL were likely to have neck and low back pain and restriction in ADL. No specific type of pain was found in patients with OPLL when compared with those who had CS.

DOI： 10.1038/s41598-020-62278-3

Open Access

Scopus

Synthetic laparoscopic video generation for machine learning-based surgical instrument segmentation from real laparoscopic video and virtual surgical instruments 査読有り国際共著

Takuya Ozawa,Yuichiro Hayashi,Hirohisa Oda, Masahiro Oda, Takayuki Kitasaka, Nobuyoshi Takeshita, Masaaki Ito, Kensaku Mori

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2020年11月

Clinical impact of Endoscopic Surgical Skill Qualification System (ESSQS) by Japan Society for Endoscopic Surgery (JSES) for laparoscopic distal gastrectomy and low anterior resection based on the National Clinical Database (NCD) registry. 招待有り査読有り Open Access

Akagi T, Endo H, Inomata M, Yamamoto H, Mori T, Kojima K, Kuroyanagi H, Sakai Y, Nakajima K, Shiroshita H, Etoh T, Saida Y, Yamamoto S, Hasegawa H, Ueno H, Kakeji Y, Miyata H, Kitagawa Y, Watanabe M

Annals of gastroenterological surgery 4 巻 ( 6 ) 頁： 721 - 734 2020年11月

Station number assignment to abdominal lymph node for assisting gastric cancer surgery 査読有り

Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2020年10月

Automated laparoscopic colorectal surgery workflow recognition using artificial intelligence: Experimental research 招待有り査読有り Open Access

Kitaguchi, D; Takeshita, N; Matsuzaki, H; Oda, T; Watanabe, M; Mori, K; Kobayashi, E; Ito, M

INTERNATIONAL JOURNAL OF SURGERY 79 巻頁： 88 - 94 2020年7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 Surgery

Background: Identifying laparoscopic surgical videos using artificial intelligence (AI) facilitates the automation of several currently time-consuming manual processes, including video analysis, indexing, and video-based skill assessment. This study aimed to construct a large annotated dataset comprising laparoscopic colorectal surgery (LCRS) videos from multiple institutions and evaluate the accuracy of automatic recognition for surgical phase, action, and tool by combining this dataset with AI. Materials and methods: A total of 300 intraoperative videos were collected from 19 high-volume centers. A series of surgical workflows were classified into 9 phases and 3 actions, and the area of 5 tools were assigned by painting. More than 82 million frames were annotated for a phase and action classification task, and 4000 frames were annotated for a tool segmentation task. Of these frames, 80% were used for the training dataset and 20% for the test dataset. A convolutional neural network (CNN) was used to analyze the videos. Intersection over union (IoU) was used as the evaluation metric for tool recognition. Results: The overall accuracies for the automatic surgical phase and action classification task were 81.0% and 83.2%, respectively. The mean IoU for the automatic tool segmentation task for 5 tools was 51.2%. Conclusions: A large annotated dataset of LCRS videos was constructed, and the phase, action, and tool were recognized with high accuracy using AI. Our dataset has potential uses in medical applications such as automatic video indexing and surgical skill assessments. Open research will assist in improving CNN models by making our dataset available in the field of computer vision.

DOI： 10.1016/j.ijsu.2020.05.015

Artificial intelligence for magnifying endoscopy, endocytoscopy, and confocal laser endomicroscopy of the colorectum 招待有り査読有り Open Access

Mori, Y; Kudo, SE; Misawa, M; Itoh, H; Oda, M; Mori, K

TECHNIQUES AND INNOVATIONS IN GASTROINTESTINAL ENDOSCOPY 22 巻 ( 2 ) 頁： 56 - 60 2020年4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：Techniques and Innovations in Gastrointestinal Endoscopy

Because magnifying endoscopy is considered to be more accurate at predicting the histology of colorectal polyps than nonmagnifying endoscopy, it has been attracting a lot of attention, especially in Japan. However, use of magnifying endoscopy is not yet widespread because of its limited availability and the difficulty in interpreting the acquired images. Application of artificial intelligence (AI) is now changing this situation because it helps less-skilled endoscopists to accurately interpret magnified images. Research in this field initially focused on magnifying endoscopy with narrow-band imaging as the target of AI. Most previously published retrospective studies have reported over 90% sensitivity in differentiation of neoplastic lesions; however, automatically indicating the region of interest (ROI) of the polyps that AI should analyze has been found to be challenging. To address this practical problem, some researchers have started to adopt contact endomicroscopy as a target for AI. Contact endomicroscopy includes endocytoscopy (520-fold magnification, Olympus, Tokyo, Japan) and confocal laser endomicroscopy (1000-fold magnification, Mauna Kea, Paris, France). These forms of contact endomicroscopy provide ultramagnified images that make it unnecessary to manually select the ROI because the entire image acquired by contact endomicroscopy is the ROI of the targeted polyps. This strength of contact endomicroscopy has contributed to early implementation of this technology into clinical practice, which may change the utility of magnifying endoscopy in clinical settings and help increase its use globally in the near future.

DOI： 10.1016/j.tgie.2019.150632

Web of Science

Scopus

Clinical application of a surgical navigation system based on virtual thoracoscopy for lung cancer patients: real time visualization of area of lung cancer before induction therapy and optimal resection line for obtaining a safe surgical margin during surgery 招待有り査読有り Open Access

Nakamura Shota, Hayashi Yuichiro, Kawaguchi Koji, Fukui Takayuki, Hakiri Shuhei, Ozeki Naoki, Mori Shunsuke, Goto Masaki, Mori Kensaku, Yokoi Kohei

JOURNAL OF THORACIC DISEASE 12 巻 ( 3 ) 頁： 672 - 679 2020年3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：Journal of Thoracic Disease

Background: We have developed a surgical navigation system that presents virtual thoracoscopic images using computed tomography (CT) image data, as if you are observing intra-thoracic cavity in synchronization with the real thoracoscopic view. Using this system, we made it possible to simultaneously visualize the ‘area of lung cancer before induction therapy’ and the ‘optimal resection line for obtaining a safe surgical margin’ as a virtual thoracoscopic view. We applied this navigation system in the clinical setting in operations for lung cancer patients with chest wall invasion after induction chemoradiotherapy. Methods: The proposed surgical navigation system consisted of a three-dimensional (3D) positional tracker and a virtual thoracoscopy system. The 3D positional tracker was used to recognize the positional information of the real thoracoscope. The virtual thoracoscopy system generated virtual thoracoscopic views based on CT image data. Combined with these two technologies, patient-to-image registration was performed in two patients, and the results generated a virtual thoracoscopic view that was synchronized with the real thoracoscopic view. Results: The operations were started with video-assisted thoracic surgery (VATS), and the navigation system was activated at the same time. The virtual thoracoscopic view was synchronized with the real thoracoscopic view, which also simultaneously indicated the ‘area of lung cancer before induction therapy’ and the ‘optimal resection lines for obtaining a safe surgical margin’. We marked the optimal lines using an electric scalpel, and then performed lobectomy and chest wall resection with a sufficient surgical margin using these landmarks. Pathological examinations confirmed that the surgical margin was negative. No complications related to the navigation system were encountered during or after the procedures. Conclusions: Using this proposed navigation system, we could obtain a ‘CT-derived virtual intrathoracic 3D view of the patient’ that was aligned with the thoracoscopic view during surgery. The accurate identification of areas of cancer invasion before induction therapy using this system might be a useful for determining optimal surgical resection lines.

DOI： 10.21037/jtd.2019.12.108

Open Access

Web of Science

Scopus

Tensor-cut: A tensor-based graph-cut blood vessel segmentation method and its application to renal artery segmentation 査読有り国際共著 Open Access

Chenglong Wang, Masahiro Oda, Yuichiro Hayashi, Yasushi Yoshino, Tokunori Yamamoto, Alejandro F. Frangi, Kensaku Mori

Medical Image Analysis 60 巻頁： 101623 2020年2月

3Dプリンティングの最新動向招待有り

森健策

インナービジョン 35 巻頁： 36 - 37 2020年

Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning 招待有り査読有り

Wang P., Shen C., Roth H.R., Yang D., Xu D., Oda M., Misawa K., Chen P.T., Liu K.L., Liao W.C., Wang W., Mori K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12444 LNCS 巻頁： 192 - 200 2020年

The performance of deep learning based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image analysis field. However, unlike photography images, it is hard to generate centralized databases to collect medical images because of numerous technical, legal, and privacy issues. In this work, we study the use of federated learning between two institutions in a real-world setting to collaboratively train a model without sharing the raw data across national boundaries. We quantitatively compare the segmentation models obtained with federated learning and local training alone. Our experimental results show that federated learning models have higher generalizability than standalone training.

DOI： 10.1007/978-3-030-60548-3_19

Scopus

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