研究者詳細 - 小田　昌宏

論文 - 小田　昌宏

分割表示 355 件中 81 - 100 件目／全件表示 >>

A skeleton context-aware 3D fully convolutional network for abdominal artery segmentation. 査読有り

Zhu R, Oda M, Hayashi Y, Kitasaka T, Misawa K, Fujiwara M, Mori K

International journal of computer assisted radiology and surgery 2022年10月

Class-wise confidence-aware active learning for laparoscopic images segmentation. 査読有り

Qiu J, Hayashi Y, Oda M, Kitasaka T, Mori K

International journal of computer assisted radiology and surgery 2022年10月

Pattern Analysis of Substantia Nigra in Parkinson Disease by Fifth-Order Tensor Decomposition and Multi-sequence MRI 査読有り

Itoh H., Hu T., Oda M., Saiki S., Kamagata K., Hattori N., Aoki S., Mori K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13594 LNCS 巻頁： 63 - 75 2022年10月

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)

This work proposes a new feature extraction method to analyse patterns of the substantia nigra in Parkinson disease. Recent imaging techniques such that neuromelanin-sensitive MRI enable us to recognise the region of the substantia nigra and capture early Parkinson-disease-related changes. However, automated feature extraction of Parkinson-disease-related changes and their geometrical interpretation are still challenging. To tackle these challenges, we introduce a fifth-order tensor expression of multi-sequence MRI data such as T1-weighted, T2-weighted, and neuromelanin images and its tensor decomposition. Reconstruction from the selected components of the decomposition visualises the discriminative patterns of the substantia nigra between normal and Parkinson-disease patients. We collected multi-sequence MRI data from 155 patients for experiments. Using the collected data, we validate the proposed method and analyse discriminative patterns in the substantia nigra. Experimental results show that the geometrical interpretation of selected features coincides with neuropathological characteristics.

DOI： 10.1007/978-3-031-18814-5_7

Scopus

Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning 査読有り国際共著

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

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13573 LNCS 巻頁： 58 - 67 2022年10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）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

Scopus

重症COVID-19肺炎における深層学習を用いたCT画像評価と臨床パラメーターの検討査読有り

坂東皓介, 春日井大介, 後藤縁, 小田昌宏, 森健策

日本救急医学会雑誌 33 巻 ( 10 ) 頁： 760 - 760 2022年10月

特集整形外科領域における人工知能の応用各論深層学習を用いたMRIでの脊髄腫瘍自動位置検出システムの構築招待有り

伊藤定之, 中島宏彰, 町野正明, 世木直喜, 小田昌宏, 大内田隼, 森下和明, 森健策, 今釜史郎

臨床整形外科 57 巻 ( 10 ) 頁： 1189 - 1195 2022年10月

Geometric Constraints for Self-supervised Monocular Depth Estimation on Laparoscopic Images with Dual-task Consistency 査読有り

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

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT IV 13434 巻頁： 467 - 477 2022年9月

Depth values are essential information to automate surgical robots and achieve Augmented Reality technology for minimally invasive surgery. Although depth-pose self-supervised monocular depth estimation performs impressively for autonomous driving scenarios, it is more challenging to predict accurate depth values for laparoscopic images due to the following two aspects: (i) the laparoscope’s motions contain many rotations, leading to pose estimation difficulties for the depth-pose learning strategy; (ii) the smooth surface reduces photometric error even if the matching pixels are inaccurate between adjacent frames. This paper proposes a novel self-supervised monocular depth estimation for laparoscopic images with geometric constraints. We predict the scene coordinates as an auxiliary task and construct dual-task consistency between the predicted depth maps and scene coordinates under a unified camera coordinate system to achieve pixel-level geometric constraints. We extend the pose estimation into a Siamese process to provide stronger and more balanced geometric constraints in a depth-pose learning strategy by leveraging the order of the adjacent frames in a video sequence. We also design a weight mask for depth estimation based on our consistency to alleviate the interference from predictions with low confidence. The experimental results showed that the proposed method outperformed the baseline on depth and pose estimation. Our code is available at https://github.com/MoriLabNU/GCDepthL.

DOI： 10.1007/978-3-031-16440-8_45

Web of Science

Scopus

Positive-gradient-weighted object activation mapping: visual explanation of object detector towards precise colorectal-polyp localisation 査読有り

Itoh Hayato, Misawa Masashi, Mori Yuichi, Kudo Shin-Ei, Oda Masahiro, Mori Kensaku

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2022年8月

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: Precise polyp detection and localisation are essential for colonoscopy diagnosis. Statistical machine learning with a large-scale data set can contribute to the construction of a computer-aided diagnosis system for the prevention of overlooking and miss-localisation of a polyp in colonoscopy. We propose new visual explaining methods for a well-trained object detector, which achieves fast and accurate polyp detection with a bounding box towards a precise automated polyp localisation. Method: We refine gradient-weighted class activation mapping for more accurate highlighting of important patterns in processing a convolutional neural network. Extending the refined mapping into multiscaled processing, we define object activation mapping that highlights important object patterns in an image for a detection task. Finally, we define polyp activation mapping to achieve precise polyp localisation by integrating adaptive local thresholding into object activation mapping. We experimentally evaluate the proposed visual explaining methods with four publicly available databases. Results: The refined mapping visualises important patterns in each convolutional layer more accurately than the original gradient-weighted class activation mapping. The object activation mapping clearly visualises important patterns in colonoscopic images for polyp detection. The polyp activation mapping localises the detected polyps in ETIS-Larib, CVC-Clinic and Kvasir-SEG database with mean Dice scores of 0.76, 0.72 and 0.72, respectively. Conclusions: We developed new visual explaining methods for a convolutional neural network by refining and extending gradient-weighted class activation mapping. Experimental results demonstrated the validity of the proposed methods by showing that accurate visualisation of important patterns and localisation of polyps in a colonoscopic image. The proposed visual explaining methods are useful for the interpreting and applying a trained polyp detector.

DOI： 10.1007/s11548-022-02696-y

Web of Science

Scopus

PubMed

テンソル分解を用いた黒質緻密部の3次元パターン表現に関する初期的検討

伊東隼人, 小田昌宏, 斉木臣二, 服部信考, 鎌形康司, 青木茂樹, 森健策

日本医用画像工学会大会予稿集 41回巻頁： 124 - 125 2022年7月

記述言語：日本語掲載種別：研究論文（研究会，シンポジウム資料等）出版者・発行元：(一社)日本医用画像工学会

黒質緻密部の3次元パターン表現に関する初期的検討について報告する.黒質緻密部における神経細胞の減少はパーキンソン病疾患者において観察される特徴のひとつである.この黒質緻密部の変化はT2強調画像や神経メラニン強調画像を介して捉えることができ,黒質緻密部の左右差や体積の減少として観察できると報告されている.一方で,得られた画像データを用いて黒質緻密部の3次元パターンの変化を計算機上で解析するためには,何らかの特徴表現が必要となる.本稿では黒質緻密部の神経メラニンMRIの信号強度比をテンソルにて表し,これらテンソルの分解に基づいた特徴表現を提案する.提案した特徴表現にて健常者・パーキンソン病患者のパターン分布を調査した結果,2つのクラスで異なる分布を表現できていることを確認した.(著者抄録)

researchmap

Comprehensive Diagnostic Performance of Real-Time Characterization of Colorectal Lesions Using an Artificial Intelligence–Assisted System: A Prospective Study 査読有り

Minegishi Y., Kudo S.E., Miyata Y., Nemoto T., Mori K., Misawa M., Mori Y., Mochida K., Akimoto Y., Ishiyama M., Ogura Y., Abe M., Sato Y., Ogawa Y., Yasuharu M., Tanaka K., Ichimasa K., Nakamura H., Ogata N., Hisayuki T., Kudo T., Hayashi T., Wakamura K., Miyachi H., Baba T., Ishida F., Itoh H., Oda M.

Gastroenterology 163 巻 ( 1 ) 頁： 323 - 325.e3 2022年7月

Blood Vessel Segmentation from Low-Contrast and Wide-Field Optical Microscopic Images of Cranial Window by Attention-Gate-Based Network 査読有り

Wu Yunheng, Oda Masahiro, Hayashi Yuichiro, Takebe Takanori, Nagata Shogo, Wang Cheng, Mori Kensaku

2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2022) 2022-June 巻頁： 1863 - 1872 2022年6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）出版者・発行元：IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

The stereomicroscope, which is an optical microscope, is used to observe the organoids cultured in cranial windows. A cranial window is a light accessible observation window made on the brain of mice through craniotomy. Organoids research is often conducted on cranial windows. Hence, the observation of blood vessels in them is important for organoid research, like organoid vascularization. Therefore, achieving a simple, low-cost method that extracts blood vessel structures would significantly help researchers observe the blood vessels in cranial windows from microscopic images. However, wide-field optical microscopic images taken by stereomicroscope suffer from low contrast and dura mater occlusion, complicating the observation of the blood vessels in such images. To address such problems and assist researchers who are observing vascular structures, we propose a method that segments blood vessels in cranial windows from low-contrast and wide-field microscopic images. Our method is based on the Attention U-Net framework and clDice, which considers the connectivity of blood vessels. In addition, for low-contrast and partial occlusion problems, we used contrast enhancement and dehazing as preprocessing steps. Our method achieved a Dice score of 75.56%, a clDice score of 79.95%, and the Accuracy of 91.41% on our microscopic image dataset, suggesting that our method can extract blood vessels from low-contrast and wide-field microscopic images better than other methods.

DOI： 10.1109/CVPRW56347.2022.00203

Web of Science

Scopus

BONE MARROW EDEMA SCORE IN HAND X-RAY FILM BY AI DEEP LEARNING ASSOCIATE WITH MRI BONE EDEMA IN RHEUMATOID ARTHRITIS.

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

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

JACLS ALL-02 SR protocol reduced-intensity chemotherapy produces excellent outcomes in patients with low-risk childhood acute lymphoblastic leukemia

Takahashi Y., Ishida H., Imamura T., Tamefusa K., Suenobu S., Usami I., Yumura-Yagi K., Hasegawa D., Nishimura S., Suzuki N., Hashii Y., Deguchi T., Moriya-Saito A., Kosaka Y., Kato K., Kobayashi R., Kawasaki H., Hori H., Sato A., Kudo T., Nakahata T., Oda M., Hara J., Horibe K.

International Journal of Hematology 115 巻 ( 6 ) 頁： 890 - 897 2022年6月

出版者・発行元：International Journal of Hematology

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. As overall cure rates of childhood ALL have improved, reduction of overall treatment intensity while still ensuring excellent outcomes is imperative for low-risk patients. We report the outcomes of patients treated following the standard-risk protocol from the prospective Japan Association of Childhood Leukemia Study (JACLS) ALL-02 study, which was conducted between 2002 and 2008 for patients with newly diagnosed ALL aged 1–18 years. Of 1138 patients with B-cell precursor ALL, 388 (34.1%) were allocated to this protocol. Excellent outcomes were achieved despite the overall treatment intensity being lower than that of most contemporary protocols: 4 years event-free survival (EFS) was 92.3% and 4 years overall survival 98.2%. Patients with high hyperdiploidy (HHD) involving triple trisomy (trisomy of chromosomes 4, 10, and 17) or ETV6-RUNX1 had even better outcomes (4 years EFS 97.6% and 100%, respectively). Unique characteristics of this protocol include a selection of low-risk patients with a low initial WBC count and good early treatment response and reduction of cumulative doses of chemotherapeutic agents while maintaining dose density. In Japan, we are currently investigating the feasibility of this protocol while incorporating minimal residual disease into the patient stratification strategy.

DOI： 10.1007/s12185-022-03315-x

Scopus

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

Zheng T, Oda H, Hayashi Y, Moriya T, Nakamura S, Mori M, Takabatake H, Natori H, Oda M, Mori K

Journal of Medical Imaging 9 巻 ( 2 ) 頁： 024003 2022年3月

A cascaded fully convolutional network framework for dilated pancreatic duct segmentation 査読有り国際共著

Shen Chen, Roth Holger R., Hayashi Yuichiro, Oda Masahiro, Miyamoto Tadaaki, Sato Gen, Mori Kensaku

International Journal of Computer Assisted Radiology and Surgery 17 巻 ( 2 ) 頁： 343 - 354 2022年2月

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

Real-time Esophagus Achalasia Detection Method for Esophagoscopy Assistance 査読有り

Jiang Kai, Oda Masahiro, Shiwaku Hironari, Misawa Masashi, Mori Kensaku

MEDICAL IMAGING 2022: COMPUTER-AIDED DIAGNOSIS 12033 巻 2022年2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）出版者・発行元：Progress in Biomedical Optics and Imaging - Proceedings of SPIE

This paper presents an automated real-time esophagus achalasia (achalasia) detection method for esophagoscopy assistance. Achalasia is a well-recognized primary esophageal motor disorder of unknown etiology. To diagnose the achalasia, endoscopic evaluation of the esophagus and stomach is recommended to ensure that there is not a malignancy causing the disease or esophageal squamous cell carcinoma complicating achalasia. However, esophagoscopy is low sensitive in the early-stage of achalasia, only about half of patients with early-stage achalasia can be identified. Thus, a quantitative detection system of real-time esophagoscopy video is required for diagnosis assistance of achalasia. This paper presents to use of a convolutional neural network (CNN) to detect all achalasia frames in esophagoscopy videos. The features of achalasia cannot be easily distinguished. To better extract features from esophagoscopy frames, we introduce dense pooling connections and dilated convolutions in the CNN. We trained and evaluated our network with an original dataset that is extracted from several esophagoscopy videos of achalasia patients. Furthermore, we develop a real-time achalasia detection ComputerAided Diagnosis (CAD) system with the trained network. The CAD system can detect each frame from the input esophagoscopy videos with only 0.1 milliseconds delay. The real-time achalasia detection system achieved 0.872 accuracy, and 0.943 AUC score on our dataset.

DOI： 10.1117/12.2613289

Web of Science

Scopus

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

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

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2022年1月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

This paper presents a new 3D-spatial feature extraction from a 2D colonoscopic image for polyp-size estimation. The polyp-size estimation poses potential demands on colonoscopy, since an endoscopist’s subjective estimation is apt to result in uncertain polyp-size determination. This uncertain determination is derived from the lack of 3D-spatial information. Even though a previous work clarified that depth estimation mitigates uncertainty in binary polyp-size classification, precise estimation of 3D structure from a colonoscopic image(s) remains an unsolved challenge in medical image analysis. This work proposes a 3D-spatial feature that expresses a polyp’s precise 3D shape to mitigate uncertainty in polyp-size determination. First, we introduce an accurate depth estimation method to capture the 3D structure of a colon. Next, we integrate depth estimation and polyp localisation to extract a 3D polyp shape as a feature. Finally, we achieve polyp-size estimation by statistical learning of extracted features. The experimental results demonstrated the validity both of our depth estimation and 3D-spatial feature. Compared with deep RGB and RGB-D convolutional neural networks (CNNs), a shallow CNN with the proposed 3D-spatial feature achieved a more accurate polyp-size estimation with a mean absolute error of 1.36 mm, whereas the one of the deep CNN is 3.11 mm.

DOI： 10.1080/21681163.2021.2004445

Web of Science

Scopus

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

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

Gastrointestinal Endoscopy 95 巻 ( 1 ) 頁： 155 - 163 2022年1月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Gastrointestinal Endoscopy

Background and Aims: Recently, the use of computer-aided detection (CADe) for colonoscopy has been investigated to improve the adenoma detection rate (ADR). We aimed to assess the efficacy of a regulatory-approved CADe in a large-scale study with high numbers of patients and endoscopists. Methods: This was a propensity score–matched prospective study that took place at a university hospital between July 2020 and December 2020. We recruited patients aged ≥20 years who were scheduled for colonoscopy. Patients with polyposis, inflammatory bowel disease, or incomplete colonoscopy were excluded. We used a regulatory-approved CADe system and conducted a propensity score matching–based comparison of the ADR between patients examined with and without CADe as the primary outcome. Results: During the study period, 2261 patients underwent colonoscopy with the CADe system or routine colonoscopy, and 172 patients were excluded in accordance with the exclusion criteria. Thirty endoscopists (9 nonexperts and 21 experts) were involved in this study. Propensity score matching was conducted using 5 factors, resulting in 1836 patients included in the analysis (918 patients in each group). The ADR was significantly higher in the CADe group than in the control group (26.4% vs 19.9%, respectively; relative risk, 1.32; 95% confidence interval, 1.12-1.57); however, there was no significant increase in the advanced neoplasia detection rate (3.7% vs 2.9%, respectively). Conclusions: The use of the CADe system for colonoscopy significantly increased the ADR in a large-scale prospective study including 30 endoscopists (Clinical trial registration number: UMIN000040677.)

DOI： 10.1016/j.gie.2021.07.022

Web of Science

Scopus

PubMed

Enhancing Model Generalization for Substantia Nigra Segmentation Using a Test-time Normalization-Based Method

Hu T., Itoh H., Oda M., Hayashi Y., Lu Z., Saiki S., Hattori N., Kamagata K., Aoki S., Kumamaru K.K., Akashi T., Mori K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13437 LNCS 巻頁： 736 - 744 2022年

掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Automatic segmentation of substantia nigra (SN), which is Parkinson’s disease-related tissue, is an important step toward accurate computer-aided diagnosis systems. Conventional methods for SN segmentation depend heavily on limited magnetic resonance imaging (MRI) modalities such as neuromelanin and quantitative susceptibility mapping, which require longer imaging times and are rare in public datasets. To enable a multi-modal investigation for SN anatomic alterations based on medical bigdata researches, the need for automated SN segmentation arises from commonly investigated T2-weighted MRIs. To improve the performance of the automated SN segmentation from a T2-weighted MRI and enhance the model generalization for cross-center researches, this paper proposes a novel test-time normalization (TTN) method to increase the geometric and intensity similarity between the query data and the model’s trained data. Our proposed method requires no additional training procedure or extra annotation for the unseen data. Our results showed that our proposed TTN achieved a mean Dice score of 71.08% in comparison with the baseline model’s 69.87% score with in-house dataset. Additionally, improved SN segmentation performance was observed from the unseen and unlabeled datasets.

DOI： 10.1007/978-3-031-16449-1_70

Scopus

researchmap

その他リンク： https://dblp.uni-trier.de/db/conf/miccai/miccai2022-7.html#HuIOHLSHKAKAM22

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

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

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2021年12月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

Depth estimation is an essential tool in obtaining depth information for robotic surgery and augmented reality technology in the current laparoscopic surgery robot system. Since there is a lack of ground-truth for depth values and laparoscope motions during operation, depth estimation networks have difficulties in predicting depth maps from laparoscopic images under the supervised strategy. It is challenging to generate the correct depth maps for the different environments from abdominal images. To tackle these problems, we propose a novel monocular self-supervised depth estimation network with sparse nest architecture. We design a non-local block to capture broader and deeper context features that can further enhance the scene-variant generalisation capacity of the network for the differences in datasets. Moreover, we introduce an improved multi-mask feature in the loss function to tackle the classical occlusion problem based on the time-series information from stereo videos. We also use heteroscedastic aleatoric uncertainty to reduce the effect of noisy data for depth estimation. We compared our proposed method with other existing methods for different scenes in datasets. The experimental results show that the proposed model outperformed the state-of-the-art models qualitatively and quantitatively.

DOI： 10.1080/21681163.2021.2015723

Web of Science

Scopus

　前のページ - 次のページ　

<小田 昌宏>

論文 - 小田 昌宏

<小田　昌宏>

論文 - 小田　昌宏