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Rapid Path Extraction and Three-Dimensional Roaming of the Virtual Endonasal Endoscope

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Chinese Journal of Electronics 2021年第3期30卷 397-405页

作者： WANG Yudong HAN Jing PAN Junjun WANG Jing CAO Yi ZHU Li LUO Yanlin School of Artificial Intelligence Beijing Normal University State Key Laboratory of Virtual Reality Technology and Systems Beihang University Institute of Applied Physics and Computational Mathematics Department of Otolaryngology Head and Neck Surgery Peking University Third Hospital

Compared with traditional endonasal endoscope, Virtual endonasal endoscope(VEE) is a computerized alternative solution with the advantages of being non-invasive, affordable, no perforation, low risk of infection, and high degree of sensitivity. A Central path extraction algorithm based on Pre-planning of the roaming area(CPE-PRA) is proposed to extract the central path of the nasal cavity model efficiently. And a B-spline curve fitting algorithm based on Relative center deviation(RCD)is introduced to fit a smooth curve to the extracted path,so that the virtual camera could have both a steady turning speed and a broad vision. A series of comparison tests were conducted to evaluate the effectiveness and efficiency of the proposed algorithms.

关键词： virtual instrumentation Virtual endonasal endoscope RCD rapid path extraction nasal cavity model medical image processing VEE feature extraction path planning CPE-PRA splines (mathematics) B-spline curve fitting algorithm computerized alternative solution roaming area relative center deviation virtual camera Central path extraction algorithm curve fitting endoscopes surgery medical robotics three-dimensional roaming

Prediction of Protein Subcellular Localization Based on Microscopic images via Multi-Task Multi-Instance Learning

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Chinese Journal of Electronics 2022年第5期31卷 888-896页

作者： ZHANG Pingyue ZHANG Mengtian LIU Hui YANG Yang Department of Computer Science and Engineering Shanghai Jiao Tong University

Protein localization information is essential for understanding protein functions and their roles in various biological processes. The image-based prediction methods of protein subcellular localization have emerged in recent years because of the advantages of microscopic images in revealing spatial expression and distribution of proteins in cells. However, the image-based prediction is a very challenging task, due to the multi-instance nature of the task and low quality of images. In this paper, we propose a multi-task learning strategy and mask generation to enhance the prediction performance. Furthermore, we also investigate effective multi-instance learning *** collect a large-scale dataset from the Human Protein Atlas database, and the experimental results show that the proposed multi-task multi-instance learning model outperforms both single-instance learning and common multi-instance learning methods by large margins.

关键词： multitask multiinstance learning model deep learning (artificial intelligence) mask generation protein subcellular localization medical image processing biological processes protein functions microscopic images spatial expression image-based prediction methods Human Protein Atlas database single-instance learning proteins cellular biophysics

Sitting or Standing Data Acquisition Based Breast Ultrasound Computed Tomography

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Chinese Journal of Electronics 2021年第5期30卷 909-917页

作者： LI Ruijing CHEN Houjin PENG Yahui LI Jupeng LI Yanfeng School of Electronic and Information Engineering Beijing Jiaotong University

Ultrasound computed tomography(UCT)is a promising approach for early breast cancer ***, current studies which use prone posture to collect breast ultrasonic data cause four problems, a long non-data-acquisition time, inconvenient, possible chances for cross infection, and a large area occupied by *** purpose of this study is to estimate a complete breast UCT image by using sitting or standing data acquisition, which can obtains a more rapid, convenient and sanitary examination process, and a less space occupied by equipment. Therefore, this study proposes a sitting or standing data acquisition based breast UCT method, which is a more practical data acquisition method for breast UCT. This study places a uniform soft sleeve on the outside of the breast so that it would not be deformed significantly due to the change of body posture. Because the soft sleeve is an influencing factor from outside for breast imaging, this study discusses the considerations for selecting that. Computer simulations are conducted to prove the effectiveness of the proposed method. Results suggest that, by using a soft sleeve whose sound speed is between 1450 m/s and 1550 m/s, the proposed method is effective; the biases of the reconstructed images are less than 1% under the 5% noise condition.

关键词： practical data acquisition method biological organs nondata-acquisition time breast imaging breast ultrasonic data data acquisition computerised tomography cancer breast ultrasound computed tomography biomedical ultrasonics acoustic tomography sanitary examination process soft sleeve image reconstruction data acquisition based breast UCT method medical image processing breast cancer screening computer simulations

Hippocampal Segmentation in Brain MRI images Using Machine Learning Methods: A Survey

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Chinese Journal of Electronics 2021年第5期30卷 793-814页

作者： PAN Yi LIU Jin TIAN Xu LAN Wei GUO Rui Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South University School of Computer Electronics and Information Guangxi University

The hippocampus is closely related to many brain diseases, such as Alzheimer’s disease. Accurate measurement of the hippocampus is helpful for clinicians in identifying lesions and then diagnosing and treating the related brain diseases. Therefore, accurate segmentation of the hippocampus is of vital significance for the indepth study of many brain diseases. However, the accurate measurement of the hippocampus depends on its accurate segmentation, and hippocampal segmentation has always been a challenging problem due to the small size,irregular shape, and fuzzy boundaries with surrounding tissues of the hippocampus. With the development of machine learning, many innovative methods have been proposed to segment the hippocampus. The purpose of this survey is to provide a comprehensive overview of hippocampal segmentation in brain MRI images using machine learning methods. First, a brief introduction to hippocampal segmentation in brain MRI images is given. Then, common evaluation metrics of hippocampal segmentation are introduced. Next, brain hippocampal segmentation methods based on traditional machine learning and deep learning are described. Subsequently,some common open datasets and toolkits applied to brain hippocampal segmentation are presented. Finally,objective conclusions regarding hippocampal segmentation in brain MRI images using machine learning methods are drawn, and future developments and trends are identified for brain hippocampal segmentation.

关键词： biomedical MRI brain image segmentation machine learning methods brain hippocampal segmentation methods medical image processing neurophysiology related brain diseases diseases learning (artificial intelligence) hippocampus brain MRI images

Deep Learning and Its Application in Diabetic Retinopathy Screening

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Chinese Journal of Electronics 2020年第6期29卷 992-1000页

作者： ZOU Beiji SHAN Xi ZHU Chengzhang DAI Yulan YUE Kejuan CHEN Yuanqiong XIAO Yalong HUANG Jiaer School of Computer Science and Engineering Central South University Hunan Engineering Research Center of Machine Vision and Intelligent Medicine Central South University The College of Literature and Journalism Central South University

Deep learning(DL),especially Convolutional neural networks(CNN),has gained wide popularity in various image processing *** the significant achievements obtained in DL,it has provided many successful solutions for real-world applications as well as in medical *** retinal images analysis has been widely applied to screening Diabetic retinopathy(DR),which can greatly help preventing the occurrence of complete blindness when used in the early *** this paper,we mainly focus on DL,and we will give an overview of the deep learning-based methods for DR ***,we will discuss the main issues encountered in the DR screening systems.

关键词： Reviews and tutorial papers Optical and laser radiation (medical uses) convolutional neural networks complete blindness Optical and laser radiation (biomedical imaging/measurement) Biology and medical computing deep learning automated retinal image analysis biomedical optical imaging diseases convolutional neural nets Patient diagnostic methods and instrumentation resource letters diabetic retinopathy screening systems Optical, image and video signal processing Computer vision and image processing techniques medical image processing eye Neural computing techniques learning (artificial intelligence) reviews

Segmentation of the Ventricle Membranes in Short-Axis Sequences by Optical Flow Base on DLSRE Model

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Chinese Journal of Electronics 2021年第3期30卷 460-470页

作者： LI Lin WU Hengfei LI Junhua Department of Electronics and Information Engineering Bozhou University Key Laboratory of Jiangxi Province for image processing and Pattern Recognition Nanchang Hangkong University

Recent studies have pointed out that the boundary of the extracted ventricle membranes is unsmooth, and the segmentation of the cardiac papillary muscle and trabecular muscle do inconformity the clinical requirements. To address these issues, this paper proposes an automatic segment algorithm for continuously extracting ventricle membranes boundary, which adopts optical flow field information and sequential images information. The images are cropped by frame difference method, which according to the continuity of adjacent slices of cardiac MRI images. The roughly boundary of epicardium is extracted by the Double level set region evolution(DLSRE) model, which combines image global information, local information and edge information. The ventricle endocardium and epicardial contours are tracked according to the optical flow field information between image sequences. The segmentation results are optimized by Delaunay triangulation algorithm. The experimental results demonstrate that the proposed method can improve the accuracy of segmenting the ventricle endocardium and epicardium contours, and segment the contour of the smooth ventricle membrane edge that meets the clinical definition.

关键词： edge information image sequences automatic segment algorithm sequential images information clinical requirements medical image processing cardiology optical flow base extracted ventricle membranes roughly boundary local information ventricle endocardium trabecular muscle optical flow field information continuously extracting ventricle membranes boundary DLSRE model biomedical MRI smooth ventricle membrane edge image global information mesh generation muscle short-axis sequences cardiac MRI images image segmentation cardiac papillary muscle frame difference method region evolution model

Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed medical images

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Computers, Materials & Continua 2022年第2期70卷 3267-3279页

作者： Seonjae Kim Dongsan Jun Department of Convergence IT Engineering Kyungnam UniversityChangwon51767Korea

medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare *** general,image compression can introduce undesired coding artifacts,such as blocking artifacts and ringing *** this paper,we proposed a Multi-Scale Feature Attention Network(MSFAN)with two essential parts,which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical *** feature extraction layers have four Feature Extraction(FE)*** FE block consists of five convolution layers and one CA block for weighted skip *** order to optimize the proposed network architectures,a variety of verification tests were conducted using validation *** used Computer Vision Center-Clinic Database(CVC-ClinicDB)consisting of 612 colonoscopy medical images to evaluate the enhancement of image *** proposedMSFAN can achieve improved PSNR gains as high as 0.25 and 0.24 dB on average compared to DnCNNand DCSC,respectively.

关键词： medical image processing convolutional neural network deep learning telemedicine artifact reduction image restoration

A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 153-166页

作者： Xiaoling Luo Wei Wang Yong Xu Zhihui Lai Xiaopeng Jin Bob Zhang David Zhang Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of TechnologyShenzhenChina Peng Cheng Laboratory ShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina College of Big Data and Internet Shenzhen Technology UniversityShenzhenChina The Department of Computer and Information Science University of MacaoMacaoMacaoChina The Chinese University of Hong Kong(Shenzhen) ShenzhenChina

Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of *** present,deep convolutional neural networks have achieved promising performance in automatic DR detection *** convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for ***,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(*** and exudates)scattered throughout the whole *** proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR ***-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as *** Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained *** experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.

关键词： image classification medical image processing pattern recognition

Two-view attention-guided convolutional neural network for mammographic image classification

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 453-467页

作者： Lilei Sun Jie Wen Junqian Wang Yong Zhao Bob Zhang Jian Wu Yong Xu College of Computer Science and Technology Guizhou UniversityGuiyangChina Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of TechnologyShenzhenChina Harbin Institute of Technology ShenzhenChina School of Electronic and Computer Engineering Shenzhen Graduate School of Peking UniversityShenzhenChina Department of Computer and Information Science University of MacaoTaipaChina Science for Life Laboratory KTH Royal Institute of TechnologyStockholmSweden

Deep learning has been widely used in the field of mammographic image classification owing to its superiority in automatic feature ***,general deep learning models cannot achieve very satisfactory classification results on mammographic images because these models are not specifically designed for mammographic images and do not take the specific traits of these images into *** exploit the essential discriminant information of mammographic images,we propose a novel classification method based on a convolutional neural ***,the proposed method designs two branches to extract the discriminative features from mammographic images from the mediolateral oblique and craniocaudal(CC)mammographic *** features extracted from the two-view mammographic images contain complementary information that enables breast cancer to be more easily ***,the attention block is introduced to capture the channel-wise information by adjusting the weight of each feature map,which is beneficial to emphasising the important features of mammographic ***,we add a penalty term based on the fuzzy cluster algorithm to the cross-entropy function,which improves the generalisation ability of the classification model by maximising the interclass distance and minimising the intraclass distance of the *** experimental results on The Digital database for Screening Mammography INbreast and MIAS mammography databases illustrate that the proposed method achieves the best classification performance and is more robust than the compared state-ofthe-art classification methods.

关键词： convolutional neural network deep learning medical image processing mammographic image