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Relationship between pulmonary nodule malignancy and surrounding pleurae, airways and vessels: a quantitative study using the public LIDC-IDRI dataset

arXiv

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arXiv 2021年

作者： Qin, Yulei Gu, Yun Zhang, Hanxiao Yang, Jie Wang, Lihui Wang, Zhexin Yao, Feng Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China CREATIS INSA Lyon CNRS UMR 5220 INSERM U1206 Université de Lyon Villeurbanne69621 France Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang550025 China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai200025 China

Objectives: To investigate whether the pleurae, airways and vessels surrounding a nodule on non-contrast computed tomography (CT) can discriminate benign and malignant pulmonary nodules. Materials and Methods: The LIDC-IDRI dataset, one of the largest publicly available CT database, was exploited for study. A total of 1556 nodules from 694 patients were involved in statistical analysis, where nodules with average scorings 3 were respectively denoted as benign and malignant. Besides, 339 nodules from 113 patients with diagnosis ground-truth were independently evaluated. computer algorithms were developed to segment pulmonary structures and quantify the distances to pleural surface, airways and vessels, as well as the counting number and normalized volume of airways and vessels near a nodule. Odds ratio (OR) and Chi-square (χ2) testing were performed to assess the correlation between features of surrounding structures and nodule malignancy. A non-parametric receiver operating characteristic (ROC) analysis was conducted in logistic regression to evaluate discrimination ability of each structure. Results: For the benign and malignant groups, the average distances from nodules to pleural surface, airways and vessels are respectively (6.56, 5.19), (37.08, 26.43) and (1.42, 1.07) mm. The correlation between nodules and the counting number of airways and vessels that contact or project towards nodules are respectively (OR=22.96, χ2=105.04) and (OR=7.06, χ2=290.11). The correlation between nodules and the volume of airways and vessels are (OR=9.19, χ2=159.02) and (OR=2.29, χ2=55.89). The areas-under-curves (AUCs) for pleurae, airways and vessels are respectively 0.5202, 0.6943 and 0.6529. Conclusion: Our results show that malignant nodules are often surrounded by more pulmonary structures compared with benign ones, suggesting that features of these structures could be viewed as lung cancer biomarkers. © 2021, CC BY.

关键词： computerized tomography

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Multiple-object Tracking based on Monocular Camera and 3-D Lidar Fusion for Autonomous Vehicles

Multiple-object Tracking based on Monocular Camera and 3-D L...

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IEEE International Conference on robotics and Biomimetics

作者： Hao Chen Chunyue Xue Shoubin Liu Yuxiang Sun Yongquan Chen Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Science and Engineering The Chinese University of Hong Kong Shenzhen China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology

This article describes a multi-object tracking method through sensor fusion with a monocular camera and a 3-D Lidar for autonomous vehicles. Specifically, several pairwise costs from information, such as locations, movements, and poses of 3-D cues, are designed for tracking. These costs can complement each other to reduce matching errors during the tracking process. Moreover, they are efficient to be on-line computed with embedded equipment. We feed the pairwise costs to the data-association framework, which is based on the Hungarian algorithm, and then do the back-end fusion for the tracking results. The experimental results on our autonomous sightseeing car demonstrate that our tracking method could achieve accurate and robust results in real-world traffic scenarios.

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Learning to generate unambiguous spatial referring expressions for real-world environments

arXiv

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arXiv 2019年

作者： Doğan, Fethiye Irmak Kalkan, Sinan Leite, Iolanda The Division of Robotics Perception and Learning The School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The KOVAN Research Lab The Department of Computer Engineering Middle East Technical University Ankara Turkey

Referring to objects in a natural and unambiguous manner is crucial for effective human-robot interaction. Previous research on learning-based referring expressions has focused primarily on comprehension tasks, while generating referring expressions is still mostly limited to rule-based methods. In this work, we propose a two-stage approach that relies on deep learning for estimating spatial relations to describe an object naturally and unambiguously with a referring expression. We compare our method to the state of the art algorithm in ambiguous environments (e.g., environments that include very similar objects with similar relationships). We show that our method generates referring expressions that people find to be more accurate (∼30% better) and would prefer to use (∼32% more often). © 2019, CC BY-NC-SA.

关键词： Human robot interaction

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Age of Information for Single Buffer Systems with Vacation Server

arXiv

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arXiv 2020年

作者： Xu, Jin Hou, I-Hong Gautam, Natarajan School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China University of Science and Technology of China Anhui Hefei China Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Industrial and Systems Engineering Texas A&M University College StationTX United States

In this research, we study the information freshness in M/G/1 queueing system with a single buffer and the server taking multiple vacations. This system has wide applications in communication systems. We aim to evaluate the information freshness in this system with both i.i.d. and non-i.i.d. vacations under three different scheduling policies, namely Conventional Buffer System (CBS), Buffer Relaxation System (BRS), and Conventional Buffer System with Preemption in Service (CBS-P). For the systems with i.i.d. vacations, we derive the closed-form expressions of information freshness metrics such as the expected Age of Information (AoI), the expected Peak Age of Information (PAoI), and the variance of peak age under each policy. For systems with non-i.i.d. vacations, we use the polling system as an example and provide the closed-form expression of its PAoI under each policy. We explore the conditions under which one of these policies has advantages over the others for each information freshness metric. We further perform numerical studies to validate our results and develop insights. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric circuit breakers

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Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge

arXiv

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arXiv 2023年

作者： Nan, Yang Xing, Xiaodan Wang, Shiyi Tang, Zeyu Felder, Federico N. Zhang, Sheng Ledda, Roberta Eufrasia Ding, Xiaoliu Yu, Ruiqi Liu, Weiping Shi, Feng Sun, Tianyang Cao, Zehong Zhang, Minghui Gu, Yun Zhang, Hanxiao Gao, Jian Wang, Pingyu Tang, Wen Yu, Pengxin Kang, Han Chen, Junqiang Lu, Xing Zhang, Boyu Mamalakis, Michail Prinzi, Francesco Carlini, Gianluca Cuneo, Lisa Banerjee, Abhirup Xing, Zhaohu Zhu, Lei Mesbah, Zacharia Jain, Dhruv Mayet, Tsiry Yuan, Hongyu Lyu, Qing Qayyum, Abdul Mazher, Moona Wells, Athol Walsh, Simon L.F. Yang, Guang Bioengineering Department and Imperial-X Imperial College London London United Kingdom Royal Brompton Hospital London United Kingdom National Heart and Lung Institute Imperial College London London United Kingdom Department of Medicine and Surgery University of Parma Parma Italy Co. Ltd. China Shanghai United Imaging Intelligence Co. Ltd. China Institute of Medical Robotics Shanghai Jiao Tong University China Department Computational Biology School of Life Sciences Fudan University Shanghai China Cambridge International Exam Centre in Shanghai Experimental School China InferVision Medical Technology Co. Ltd. China Shanghai MediWorks Precision Instruments Co. Ltd China Sanmed Biotech Ltd. Zhuhai China University of Cambridge Department of Psychiatry Cambridge United Kingdom Department of Biomedicine University of Palermo Palermo Italy IRCCS Istituto delle Scienze Neurologiche di Bologna Bologna Italy Istituto Italiano di Tecnologia Nanoscopy Genova Italy Institute of Biomedical Engineering Department of Engineering Science University of Oxford United Kingdom Guangzhou China INSA Rouen Normandie Univ Rouen Normandie Université Le Havre Normandie France Nuclear Medicine Department Henri Becquerel Cancer Center Rouen France Department of Radiology Wake Forest University School of Medicine United States Department of Computer Science University College London United Kingdom School of Biomedical Engineering & Imaging Sciences King's College London United Kingdom

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online Copyright © 2023, The Authors. All rights reserved.

关键词： Biological organs

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A CNN-LSTM hybrid framework for wrist kinematics estimation using surface electromyography

arXiv

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arXiv 2019年

作者： Bao, Tianzhe Zaidi, Syed Ali Raza Xie, Shengquan Yang, Pengfei Zhang, Zhiqiang Institute of Robotics Autonomous System and Sensing School of Electrical and Electronic Engineering University of Leeds LS2 9JT United Kingdom School of Computer Science and Technology Xidian University China

—Convolutional neural network (CNN) has been widely exploited for simultaneous and proportional myoelectric control due to its capability of deriving informative, representative and transferable features from surface electromyography (sEMG). However, muscle contractions have strong temporal dependencies but conventional CNN can only exploit spatial correlations. Considering that long short-term memory neural network (LSTM) is able to capture long-term and non-linear dynamics of time-series data, in this paper we propose a CNN-LSTM hybrid framework to fully explore the temporal-spatial information in sEMG. Firstly, CNN is utilized to extract deep features from sEMG spectrum, then these features are processed via LSTM-based sequence regression to estimate wrist kinematics. Six healthy participants are recruited for the participatory collection and motion analysis under various experimental setups. Estimation results in both intra-session and inter-session evaluations illustrate that CNN-LSTM significantly outperforms CNN and conventional machine learning approaches, particularly when complex wrist movements are activated. Copyright © 2019, The Authors. All rights reserved.

关键词： Kinematics

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Correction to: The effects of gender, age, and videogame experience on performance and experiences with a surgical robotic arm: an exploratory study with general public

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Journal of robotic surgery 2022年第3期16卷 631页

作者： Selen Türkay Kate Letheren Jeremy Opie Ross Crawford Jonathan Roberts Anjali Tumkur Jaiprakash School of Computer Science Queensland University of Technology Brisbane QLD Australia. Centre for Behavioural Economics Society and Technology Queensland University of Technology Brisbane QLD Australia. Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London (UCL) London UK. Centre for Biomedical Technologies (CBT) Queensland University of Technology Brisbane QLD Australia. School of Electrical Engineering and Robotics Queensland University of Technology Brisbane QLD Australia. Centre for Biomedical Technologies (CBT) Queensland University of Technology Brisbane QLD Australia. anjali.jaiprakash@qut.edu.au. Faculty of Health School of Clinical Sciences Queensland University of Technology Brisbane QLD Australia. anjali.jaiprakash@qut.edu.au.

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Learning tubule-sensitive CNNs for pulmonary airway and artery-vein segmentation in CT

arXiv

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arXiv 2020年

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Yao, Feng Zhu, Yue-Min Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai China Université de Lyon INSA Lyon CREATIS CNRS INSERM UMR 5220 VilleurbanneU1206 France Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce representation learning of tubular objects. Fine-grained details in high-resolution attention maps are passing down from one layer to its previous layer recursively to enrich context. Anatomy prior of lung context map and distance transform map is designed and incorporated for better artery-vein differentiation capacity. Extensive experiments demonstrated considerable performance gains brought by these components. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. Codes and models are available at http://***/News/56. © 2020, CC BY.

关键词： computerized tomography

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Perovskite superlattices with efficient carrier dynamics

Research Square

引用

Research Square 2021年

作者： Xu, Sheng Lei, Yusheng Li, Yuheng Lu, Chengchangfeng Yan, Qizhang Gong, Huaxin Zhang, Song Zhou, Jiayun Zhang, Ruiqi Chen, Yimu Tsai, Hsinhan Gu, Yue Hu, Hongjie Lo, Yu-Hwa Nie, Wanyi Lee, Taeyoon Luo, Jian Yang, Kesong Jang, Kyung-In Department of Nanoengineering University of California San Diego La Jolla CA92093-0448 United States Department of Chemical Engineering Stanford University StanfordCA94305 United States Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92093 United States Material Science and Engineering Program University of California San Diego La Jolla CA92093-0418 United States Los Alamos National Laboratory Los AlamosNM87545 United States School of Electrical and Electronic Engineering Yonsei University Seoul03722 Korea Republic of Department of Bio and Brain Engineering Korea Institute of Science and Technology Korea Republic of Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology Daegu42988 Korea Republic of Department of Bioengineering University of California San Diego La Jolla CA92093-0412 United States

Compared with their three-dimensional counterparts, low-dimensional metal halide perovskites with periodic inorganic/organic structures have shown promising stability and hysteresis-free electrical performance, which paves the way for next-generation optoelectronic devices. However, when integrated in devices, they have relatively limited efficiencies because devices usually require carrier transport through the film thickness direction. In conventionally grown single crystals, the carrier transport in the thickness direction is hindered by the insulating organic spacers. In addition, the strong quantum confinement from the organic spacers limits the generation and transport of free carriers. The carrier dynamics is further compromised by the presence of grain boundaries in polycrystals. Here, we report a low-dimensional metal halide perovskite superlattice with efficient carrier transport in three dimensions by epitaxial growth. Epitaxy on a slightly lattice-mismatched substrate compresses the organic spacers in the superlattice, which weakens the quantum confinement and further improves carrier dynamics. The performance of a low-dimensional perovskite superlattice solar cell has been certified under the quasi-steady state for the first time. Moreover, the device shows an unusually high open-circuit voltage, due to a unique intra-band exciton relaxation mechanism. © 2021, CC BY.

关键词： Grain boundaries

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Corrigendum to "PSFHS challenge report: pubic symphysis and fetal head segmentation from intrapartum ultrasound images" [Medical Image Analysis 99 (2025),103353]

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Medical image analysis 2025年 104卷 103651页

作者： Jieyun Bai Zihao Zhou Zhanhong Ou Gregor Koehler Raphael Stock Klaus Maier-Hein Marawan Elbatel Robert Martí Xiaomeng Li Yaoyang Qiu Panjie Gou Gongping Chen Lei Zhao Jianxun Zhang Yu Dai Fangyijie Wang Guénolé Silvestre Kathleen Curran Hongkun Sun Jing Xu Pengzhou Cai Lu Jiang Libin Lan Dong Ni Mei Zhong Gaowen Chen Víctor M Campello Yaosheng Lu Karim Lekadir Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China Auckland Bioengineering Institute The University of Auckland Auckland New Zealand. Electronic address: jbai996@aucklanduni.ac.nz. Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Computer Science and Engineering The Chinese University of Hong Kong China. Computer Vision and Robotics Group University of Girona Girona Spain. Canon Medical Systems (China) Co. LTD Beijing China. College of Artificial Intelligence Nankai University Tianjin China. College of Computer Science and Electronic Engineering Hunan University Changsha China. School of Medicine University College Dublin Dublin Ireland. School of Computer Science University College Dublin Dublin Ireland. School of Statistics & Mathematics Zhejiang Gongshang University Hangzhou China. School of Computer Science & Engineering Chongqing University of Technology Chongqing China. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound & Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging & School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China. NanFang Hospital of Southern Medical University Guangzhou China. Zhujiang Hospital of Southern Medical University Guangzhou China. Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain. Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China. Electronic address: tluys@***. Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain.

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