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arXiv 2020年

作者： Liu, Shaolei Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University China Shanghai Key Laboratory of Medical Image Computing and Computer Assited Intervention

We propose an unsupervised image fusion architecture for multiple application scenarios based on the combination of multi-scale discrete wavelet transform through regional energy and deep learning. To our best knowledge, this is the first time that a conventional frequency method has been combined with deep learning for feature maps fusion. The useful information of feature maps can be utilized adequately through multi-scale discrete wavelet transform in our proposed method. Compared with other state-of-the-art fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation. Moreover, it's worth mentioning that comparable fusion performance trained in COCO dataset can be obtained by training with a much smaller dataset with only hundreds of images chosen randomly from COCO. Hence, the training time is shortened substantially, leading to the improvement of the model's performance both in practicality and training efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： image fusion

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Automated brain tumor segmentation in magnetic resonance imaging based on sliding-window technique and symmetry analysis

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Chinese medical Journal 2014年第3期127卷 462-468页

作者： Lian Yanyun Song Zhijian Digital Medical Research Center Shanghai Medical School Fudan University Shanghai 200032 China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention

Background Brain tumor segmentation from magnetic resonance imaging （MRI） is an important step toward surgical planning,treatment planning,monitoring of ***,manual tumor segmentation commonly used in clinic is time-consuming and challenging,and none of the existed automated methods are highly robust,reliable and efficient in clinic *** accurate and automated tumor segmentation method has been developed for brain tumor segmentation that will provide reproducible and objective results close to manual segmentation *** Based on the symmetry of human brain,we employed sliding-window technique and correlation coefficient to locate the tumor *** first,the image to be segmented was normalized,rotated,denoised,and ***,through vertical and horizontal sliding-windows technique in turn,that is,two windows in the left and the right part of brain image moving simultaneously pixel by pixel in two parts of brain image,along with calculating of correlation coefficient of two windows,two windows with minimal correlation coefficient were obtained,and the window with bigger average gray value is the location of tumor and the pixel with biggest gray value is the locating point of *** last,the segmentation threshold was decided by the average gray value of the pixels in the square with center at the locating point and 10 pixels of side length,and threshold segmentation and morphological operations were used to acquire the final tumor *** The method was evaluated on 3D FSPGR brain MR images of 10 *** a result,the average ratio of correct location was 93.4％ for 575 slices containing tumor,the average Dice similarity coefficient was 0.77 for one scan,and the average time spent on one scan was 40 *** An fully automated,simple and efficient segmentation method for brain tumor is proposed and promising for future clinic *** coefficient is a new and effective feature for tumor locati

关键词： segmentation magnetic resonance imaging brain tumor sliding-window correlation coefficient

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IMAGGS: a radiogenomic framework for identifying multi-way associations in breast cancer subtypes

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Journal of Genetics and Genomics 2024年第4期51卷 443-453页

作者： Shuyu Liang Sicheng Xu Shichong Zhou Cai Chang Zhiming Shao Yuanyuan Wang Sheng Chen Yunxia Huang Yi Guo Department of Electronic Engineering School of Information Science and TechnologyFudan UniversityShanghai 200433China The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention(MICCAI)of Shanghai Shanghai 200032China Shanghai Key Laboratory of Metabolic Remodeling and Health Institute of Metabolism and Integrative BiologyFudan UniversityShanghai 200433China Department of Ultrasound Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai 200032China Department of Breast Surgery Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai 200032China

Investigating correlations between radiomic and genomic profiling in breast cancer(BC)molecular subtypes is crucial for understanding disease mechanisms and providing personalized *** present a well-designed radiogenomic framework image–gene–gene set(IMAGGS),which detects multi-way associations in BC subtypes by integrating radiomic and genomic *** dataset consists of 721 patients,each of whom has 12 ultrasound(US)images captured from different angles and gene mutation *** better characterize tumor traits,12 multi-angle US images are fused using two distinct ***,we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm,deviating from the prevalent one-to-one relationship pattern observed in previous *** radiomic and genomic features are screened using these *** addition,gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC *** further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and *** together,IMAGGS provides a comprehensive characterization for diseases by associating imaging,genes,and gene sets,paving the way for biological interpretation of radiomics and development of targeted therapy.

关键词： IMAGGS Radiogenomic framework "lmage-gene-geneset"associations Molecular subtypes Breast cancer

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Motion Classification Based on sEMG Signals Using Deep Learning 4th

Motion Classification Based on sEMG Signals Using Deep Learn...

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4th International Conference on Machine Learning and Intelligent Communications, MLICOM 2019

作者： Shen, Shu Gu, Kang Chen, Xinrong Wang, Ruchuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210023 China Academy for Engineering and Technology Fudan University Shanghai200433 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China

ISBN: (纸本)9783030323875

Nowadays, surface electromyography (sEMG) signal plays an important role in helping physically disabled people during daily life. The development of electronic information technology has also led to the emergence of low-cost, multi-channel, wearable sEMG signal acquisition devices. Therefore, this paper proposes a new motion recognition model based on deep learning to improve the accuracy of motion recognition of sEMG signals. The model uses architecture including 6 convolutional layers and 6 pooling layers which enables the Batch Normalization layer to enhance network performance and prevent overfitting. In the experiment, NinaPro DB5 data set was used for training and testing. The data set consists of sEMG signal data collected by the double Myo armbands which contains data of 52 movements for 10 subjects. The results show that the accuracy of about 90% can be achieved when using 52 sEMG signal data from every single subject or all subjects. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Deep learning

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A Convenient Method of Video See-Through Augmented Reality Based on image-Guided Surgery System

A Convenient Method of Video See-Through Augmented Reality B...

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International Conference on Internet computing in Science and Engineering (ICICSE)

作者： Liang Hu Manning Wang Zhijian Song Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Shanghai China

Augmented Reality (AR) is a technology which is widely used in medical area, especially in surgery room. In many cases, cumbersome equipments or complicated algorithm are introduced to establish the whole system, which would make it inefficient and unsuitable for surgery environment. This paper presents a convenient method for video see-through augmented reality on the basis of existing image-guided surgery system developed by our research center. We describe the prototype of the system, the registration of virtual objects in a marker coordinate system, and the motion tracking of the Head Mounted Display (HMD) using A Toolkit. Finally, we present the experiment results which show that the proposed AR technology can provide augmented information for the surgeons and work well with existing image-guided surgery system.

关键词： Surgery Augmented reality Tracking Biomedical optical imaging Optical imaging Calibration Cameras

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A Feasible Way to Assess the Lower Extremity Motor Function of Stroke Patients - A Preliminary Study 15

A Feasible Way to Assess the Lower Extremity Motor Function ...

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15th International Congress on image and Signal Processing, Biomedical Engineering and Informatics, CISP-BMEI 2022

作者： Liu, Sen Xie, Hongyu Yuan, Han Tian, Jing Yang, Cuiwei Wu, Yi Center for Biomedical Engineering Fudan University Shanghai China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Accurate assessment of the mobility of stroke patients is crucial to their rehabilitation management. In order to quantify the lower extremity motor function in stroke sufferers, two tasks, toe tapping and knee flexion and extension, were designed based on the Brunnstrom approach. In this work, five hemiplegic patients in Brunnstrom stage III or IV were recruited. Acceleration signals under different tasks were recorded using tri-axial accelerometers, which were placed at the arches of the feet. Various kinematic parameters were then calculated, and a statistical test was conveyed to investigate the correlations between Brunnstrom stages and the features. The results showed that in the toe tapping task, the dominant frequency of the y-axis and the proportion of signal power in specific frequency band of the x-axis were significantly higher in stage III than in stage IV. On the contrary, the standard deviation and the skewness of the y-axis were lower in stage III. Besides, there were also significant differences in the amplitude of knee flexion between cohorts at different Brunnstrom stages. It is expected that the findings of this study would be helpful for the monitoring and rehabilitation of stroke patients. © 2022 IEEE.

关键词： Patient rehabilitation

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Intelligent Evaluation of the Course of Atrial Fibrillation Using Transfer Learning 15

Intelligent Evaluation of the Course of Atrial Fibrillation ...

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15th International Congress on image and Signal Processing, Biomedical Engineering and Informatics, CISP-BMEI 2022

作者： Sun, Chen Li, Chunpu Huo, Hongye Zhong, Gaoyan Liu, Sen Wang, Aiguo Deng, Xintao Yang, Cuiwei Fudan University Center for Biomedical Engineering Shanghai China Xinghua City People's Hospital Department of Cardiology Jiangsu China The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Atrial fibrillation (AF) is the most common chronic cardiovascular disease. As the disease progresses, AF could have various impact on the quality of life and the health of patients. The scientific management of AF depends on the development of targeted therapeutic measures according to the disease course. In view of the noninvasiveness and convenience of electrocardiogram (ECG), this study attempted to assess the course of AF based on standard 12-lead ECG. Therefore, based on transfer learning, we used MobileNetV2 to achieve automatic grading of AF treatment. By taking 12-lead ECG images of patients with mild and severe AF as network input, the pre-trained model was retrained to recognize different courses. The results show that our proposed method exhibits excellent performance with accuracy of 84.1 %, precision of 86.5%, recall of 83.1%, specificity of 85.3%, and F1-score of 84.8%. Our study provides a feasible way of assessing the course of AF and is of great significance to promote the graded diagnosis and treatment of AF. © 2022 IEEE.

关键词： Electrocardiograms

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Research of an Electromagnetic Tracking Method Based on Two Electrically-controlled Magnetic Sources

Research of an Electromagnetic Tracking Method Based on Two ...

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2015 IEEE Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2015)

作者： Min Sha Yifeng Wang Ning Ding Xiaomei Wu Zuxiang Fang Department of Electronic Engineering Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

ISBN: (纸本)9781479919819

This paper presents an electromagnetic tracking method based on two electrically-controlled magnetic *** source consists of three mutual orthogonal *** method can skillfully determine the rotation angles of the maximum magnetic flux density vector(MMFD V) generated by a magnetic source according to the Biot-Savart *** the position of the sensor can be calculated with a noniterative geometrical algorithm in real *** results demonstrate a good performance of this method.

关键词： electromagnetic tracking MMFDV fast determination algorithm finite element analysis

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3D Clothed Human Reconstruction From One In-the-Wild RGB image

3D Clothed Human Reconstruction From One In-the-Wild RGB Ima...

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IEEE International Conference on image Processing

作者： Liangjing Shao Benshuang Chen Xinrong Chen Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

In recent years, much achievement have been made in the field of 3D clothed human reconstruction. However, most of researches performed not well for reconstruction from in-the-wild images due to the domain gap between the synthetic images of training datasets and the in-the-wild images. In this study, a modular model, including clothes encoder, body encoder and cloth generator, is proposed to perform 3D clothed human reconstruction from one single-view in-the-wild RGB image. In particular, we introduce the adaptive aggregation of convolution and multi-head attention into the cloth encoder and apply the adjustment of the segmentation at the preprocessing stage. According to experiments on MSCOCO and 3DPW datasets, the proposed method achieves state-of-the-art performance on 3D clothed human reconstruction from in-the-wild images compared with previous works.

关键词： Training Solid modeling image segmentation Adaptation models Three-dimensional displays Convolution Data preprocessing

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Intelligent Detection of Hypokalemia Based on 12-Lead ECG Using Two-stream Deep Learning Model 15

Intelligent Detection of Hypokalemia Based on 12-Lead ECG Us...

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15th International Congress on image and Signal Processing, Biomedical Engineering and Informatics, CISP-BMEI 2022

作者： Wang, Yueyi Zhong, Gaoyan Wang, Ya'nan Zhu, Qiang Xie, Jiayong Deng, Xintao Wang, Aiguo Yang, Cuiwei School of Information Science and Technology Fudan University Center for Biomedical Engineering Shanghai China Xinghua City People's Hospital Jiangsu China The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Hypokalemia is one of the most common electrolyte disorders in clinic. The detection of hypokalemia mainly depends on the detection of serum potassium concentration. Previous studies have shown that with the decrease of serum potassium ion concentration, ECG will show corresponding characteristics. In this paper, 12-lead ECG is used for intelligent detection of hypokalemia. After six artificial features based on ECG are extracted, a two-stream deep learning model is trained by using these features and 12-lead ECG to detect hypokalemia. The AVC of the two-stream model on the verification set is 0.84, and the AVC on the test set is 0.82. After taking the best working point, on the verification set, the sensitivity is 81.45%, the specificity is 74.21 %, and the recognition accuracy is 77.82%, while on the test set, the sensitivity is 77.54%, the specificity is 74.28%, and the recognition accuracy is 75.91 %. The results show that these time-domain features can significantly improve the recognition accuracy of hypokalemia. © 2022 IEEE.

关键词： Electrocardiograms

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