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European Signal Processing Conference (EUSIPCO)

作者： Siqing Nie Jinhua Yu Ping Chen Jianqiu Zhang Yuanyuan Wang Department of Electronic Engineering Fudan University Shanghai China Key laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Ultrasound Department Tongji University Shanghai China

In this paper, we propose a novel method for the automatic detection of fetal head in 2D ultrasound images. Fetal head detection has been a challenging task, as the ultrasound images usually have poor quality, the structures contained in the images are complex, and the gray scale distribution is highly variable. Our approach is based on a deep belief network and a modified circle detection method. The whole process can be divided into two steps: first, a deep learning architecture is applied to search the whole image and determine the result patch that contains the entire fetal head; second, a modified circle detection method is used along with Hough transform to detect the position and size of the fetal head. In order to validate our method, experiments are performed on both synthetic data and clinic ultrasound data. A good performance of the proposed method is shown in the paper.

关键词： Head image edge detection Ultrasonic imaging Magnetic heads Training Europe Signal processing

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC medical IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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A Fast Global Maximum Searching Strategy in Electromagnetic Tracking

A Fast Global Maximum Searching Strategy in Electromagnetic ...

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2015 World Conference on Control, Electronics and Electrical Engineering(WCEE 2015)

作者： Yifeng Wang Xiaomei Wu Jianguo Yu Department of Electronic Engineering Fudan University Digital Medical Research Center of Fudan University Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI)

In a rotating coil based magnetic tracking system,so long as the rotating coil has seized the direction of the magnetic sensor,geometric algorithm can compute the position and orientation of the target object without tedious iterative ***,the rotation process of the coil is still time *** the coil will be rotated in a plane,the measured magnetic field density should change as a sine/cosine-like *** on this,we proposes a searching strategy in which field densities are measured along four sparse even spaced directions to lock an initial searching range,and then the searching range is continuously halved around the measured maximum field density until a required tracking precision is *** results show that the geometric tracking algorithm with this global searching strategy can track the target object with higher efficiency and higher precision than the algorithm without this searching ***,in the condition of low field noises,the optimized non-iterative algorithm with global searching strategy has better precision than the traditional iterative algorithms as well.

关键词： adaptive algorithm,binary search,global optimization,local optimization,magnetic tracking,non-iterative algorithm.

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Segmentation of arteriovenous malformations nidus and vessel in digital subtraction angiography images based on an iterative thresholding method

Segmentation of arteriovenous malformations nidus and vessel...

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International Conference on Biomedical Engineering and Informatics (BMEI)

作者： Yuxi Lian Yuanyuan Wang Jinhua Yu Yi Guo Liang Chen Department of Electronic Engineering Fudan University Shanghai China Key laboratory of Medical Imaging Computing and-Computer Assisted Intervention of Shanghai China Department of Neurosurgery Fudan University Shanghai China

Digital subtraction angiography (DSA) plays an important role in the diagnosis and therapy of vascular diseases. Segmentation of nidus and vessel in DSA images is an essential step in the diagnosis of arteriovenous malformations (AVM). In this paper, a novel segmentation method based on the global and iterative local thresholding is proposed to segment the nidus and vessel in DSA images. Firstly, the original image is divided into proper subimages. For each subimage, Ostu's method is primarily used and pixels are classified into two groups by the threshold. Then, according to the variance of the subimage intensities, the mean or median values of two groups are calculated to sort the pixels into three classes. These three classes represent the dark AVM and vessel, the bright background and undetermined regions in the original DSA image. The first two classes are determined directly and will not be processed further. The undetermined regions are processed in the next iteration to segment tiny vessels until the thresholds between two iterations are less than a preset one. Finally, all classes are combined to create the segmentation result. We test this method on DSA images of the AVM. Experimental results show that the proposed method performs better than the other state-of-the-art methods in the segmentation of DSA images. The proposed method can identify fine and tiny vessel structures, as well as distinguish large AVM nidus in one framework.

关键词： image segmentation Iterative methods Angiography Diseases Blood vessels Hospitals

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Morphological Evidence of Telocytes in Mice Aorta

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Chinese medical Journal 2015年第3期128卷 348-352页

作者： Hong-Qi Zhang Shan-Shan Lu Ting Xu Yan-Ling Feng Hua Li Jun-Bo Ge Department of Anatomy Histology and Embryology Shanghai Medical College Fudan University Shanghai 200032 China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai 200032 China Department of Pathology Shanghai Public Health Clinical Center Shanghai 201508 China Institute of Biochemistry and Cell Biology Shanghai Institutes of Biological Sciences Chinese Academy of Sciences Shanghai 200031 China Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University Shanghai 200032 China institutes of Biomedical Sciences Fudan University Shanghai 200032 China

Background： Telocytes （TCs） are a novel type of interstitial cells, which have been recently described in a large variety of cavitary and noncavitary organs. TCs have small cell bodies, and remarkably thin, long, and moniliform prolongations called telopodes （Tps）. Until now, TCs have been found in various loose connective tissues surrounding the arterioles, venules, and capillaries, but as a histological cellular component, whether TCs exist in large arteries remains unexplored. Methods： TCs were identified by transmission electron microscope in the aortic arch of male C57BL/6 mice. Results： TCs in aortic arch had small cell bodies （length： 6.06-13.02 μm： width： 1.05-4.25 μm） with characteristics of specific long （7.74-39.05 μm）, thin, and moniliform Tps; TCs distributed in the whole connective tissue layer of tunica adventitia： TCs in the innermost layer of tunica adventitia, located at the juncture between media and adventitia, with their long axes oriented parallel to the outer elastic membrane： and TCs in outer layers oftunica adventitia, were embedded among transverse and longitudinal oriented collagen fibers,forming a highly complex three-dimensional meshwork. Moreover, desmosomes were observed, serving as pathways connecting neighboring Tps. In addition, vesicles shed from the surface of TCs into the extracellular matrix, participating in some biological processes. Conclusions： TCs in aorta arch are a newly recognized complement distinct from other interstitial cells in large arteries, such as fibroblasts. And further biologically functional correlations need to be elucidated.

关键词： Aorta＇ Telocyte： Telopode

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Low-energy Defibrillation with Multi-electrodes Stimulation: A Simulation Study

Low-energy Defibrillation with Multi-electrodes Stimulation:...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Lian Jin Jianfei Wang Biao Song Xiaomei Wu Zuxiang Fang the Department of Electronic Engineering Fudan University Shanghai China the Electrical Engineering Department Fudan University and the Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai CO 200433 China the Electrical Engineering Department Fudan University Shanghai CO 200433 China

ISBN: (纸本)9781424492695

The objective of this study is to explore the possible ways to reduce defibrillation energy and further reveal the mechanism of electric defibrillation. A bidomain simulation study was performed on a rabbit whole-ventricle electrophysiological model and the feasibility of the defibrillation strategy with multi-electrodes stimulation was verified. Simulation results indicate that the new approach is effective in low-energy defibrillation.

关键词： possible ways further reveal mechanism

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Surface-based automatic coarse registration of head scans

Surface-based automatic coarse registration of head scans

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作者： Li, Fang Song, Zhijian Digital Medical Research Center Fudan University Shanghai200032 China Key Laboratory of Medical Imaging Computing Computer Assisted Intervention of Shanghai Shanghai200032 China

Surface registration is widely used in image-guided neurosurgery to achieve spatial registration between the patient space and the image space. Coarse registration, followed by fine registration, is an important premise to ensure the robustness and efficiency of surface registration. In this paper, a coarse registration algorithm based on the principal axes is proposed to achieve this goal. The extraction of the principal axes relies on the approximated surface with an adaptive Gaussian kernel, the width of which is consistent with neighborhood relation so that it is applicable for various scanning data. Determining the corresponding centers of translation is another problem for aligning different scanning data, which is solved through heuristics. Six pairs of points on two surfaces with the farthest projections on the principal axes were regarded as the candidates of translation centers, and then through tentative alignments of local regions around them, a pair of candidates with the minimum registration error was selected as the optimal translation centers. Automatic registration of two scans of a head phantom is presented in this paper. Experimental results confirmed the robustness of the algorithm and its feasibility in clinical applications. © 2014 - IOS Press and the authors.

关键词： Neurosurgery

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超声背散射松质骨诊断仪及其在新生儿骨质评价中的应用

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Engineering 2015年第3期 123-137页

作者：刘成成张蓉李颖徐峰他得安王威琪 Department of Electronic Engineering Fudan University Department of Neonatology Children’s Hospital of Fudan University Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai

超声背散射技术在松质骨骨质无损评价中极具前景。本文介绍了一种新型超声背散射骨质诊断仪(UBBD)及其在新生儿骨质状况评价中的应用。超声背散射骨质诊断仪具有无损、无电离辐射、便携及操作简便等优点,可以在短时间内(5 s)获得人体松... 详细信息

超声背散射技术在松质骨骨质无损评价中极具前景。本文介绍了一种新型超声背散射骨质诊断仪(UBBD)及其在新生儿骨质状况评价中的应用。超声背散射骨质诊断仪具有无损、无电离辐射、便携及操作简便等优点,可以在短时间内(5 s)获得人体松质骨背散射信号。笔者共采集了467例新生儿(268男/199女)左侧跟骨处的背散射信号,所用探头中心频率为3.5 MHz。逐步调节背散射有效信号(SOI)的延迟(T1)和长度(T2),计算表观积分背散射(AIB)、表观背散射频率斜率(FSAB)及零频率截距(FIAB)和频谱质心偏移量(SCS)等参数。结果表明,背散射有效信号的选取对背散射测量有直接的影响。当T1较短时(<8μs),AIB和FIAB与新生儿胎龄有显著的正相关性(|R|=0.45,P<0.001);而当T1较长时(>10μs),AIB和FIAB负相关于新生儿胎龄(|R|=0.56,P<0.001),还可以观察到FSAB和SCS与新生儿胎龄有中度的正相关性(|R|=0.45,P<0.001)。T2对背散射测量的影响较小,引起相关系数的波动较小。本文通过自制的松质骨超声诊断仪证明超声背散射信号用于评价新生儿骨质状况的可行性。本文还提出了明确的背散射有效信号选取规则及新生儿骨质评价标准。

关键词：超声背散射松质骨评价有效信号背散射仪器新生儿骨质

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Publisher Correction: Brain charts for the human lifespan

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Nature 2022年第7931期610卷 E6页

作者： R A I Bethlehem J Seidlitz S R White J W Vogel K M Anderson C Adamson S Adler G S Alexopoulos E Anagnostou A Areces-Gonzalez D E Astle B Auyeung M Ayub J Bae G Ball S Baron-Cohen R Beare S A Bedford V Benegal F Beyer J Blangero M Blesa Cábez J P Boardman M Borzage J F Bosch-Bayard N Bourke V D Calhoun M M Chakravarty C Chen C Chertavian G Chetelat Y S Chong J H Cole A Corvin M Costantino E Courchesne F Crivello V L Cropley J Crosbie N Crossley M Delarue R Delorme S Desrivieres G A Devenyi M A Di Biase R Dolan K A Donald G Donohoe K Dunlop A D Edwards J T Elison C T Ellis J A Elman L Eyler D A Fair E Feczko P C Fletcher P Fonagy C E Franz L Galan-Garcia A Gholipour J Giedd J H Gilmore D C Glahn I M Goodyer P E Grant N A Groenewold F M Gunning R E Gur R C Gur C F Hammill O Hansson T Hedden A Heinz R N Henson K Heuer J Hoare B Holla A J Holmes R Holt H Huang K Im J Ipser C R Jack A P Jackowski T Jia K A Johnson P B Jones D T Jones R S Kahn H Karlsson L Karlsson R Kawashima E A Kelley S Kern K W Kim M G Kitzbichler W S Kremen F Lalonde B Landeau S Lee J Lerch J D Lewis J Li W Liao C Liston M V Lombardo J Lv C Lynch T T Mallard M Marcelis R D Markello S R Mathias B Mazoyer P McGuire M J Meaney A Mechelli N Medic B Misic S E Morgan D Mothersill J Nigg M Q W Ong C Ortinau R Ossenkoppele M Ouyang L Palaniyappan L Paly P M Pan C Pantelis M M Park T Paus Z Pausova D Paz-Linares A Pichet Binette K Pierce X Qian J Qiu A Qiu A Raznahan T Rittman A Rodrigue C K Rollins R Romero-Garcia L Ronan M D Rosenberg D H Rowitch G A Salum T D Satterthwaite H L Schaare R J Schachar A P Schultz G Schumann M Schöll D Sharp R T Shinohara I Skoog C D Smyser R A Sperling D J Stein A Stolicyn J Suckling G Sullivan Y Taki B Thyreau R Toro N Traut K A Tsvetanov N B Turk-Browne J J Tuulari C Tzourio É Vachon-Presseau M J Valdes-Sosa P A Valdes-Sosa S L Valk T van Amelsvoort S N Vandekar L Vasung L W Victoria S Villeneuve A Villringer P E Vértes K Wagstyl Y S Wang S K Warfield V Warrier E Westman M L Westwater H C Whalley A V Witte N Yang B Yeo H Y Autism Research Centre Department of Psychiatry University of Cambridge Cambridge UK. rb643@medschl.cam.ac.uk. Brain Mapping Unit Department of Psychiatry University of Cambridge Cambridge UK. rb643@medschl.cam.ac.uk. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Department of Child and Adolescent Psychiatry and Behavioral Science The Children's Hospital of Philadelphia Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Lifespan Brain Institute The Children's Hospital of Philadelphia and Penn Medicine Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Department of Psychiatry University of Cambridge Cambridge UK. MRC Biostatistics Unit University of Cambridge Cambridge UK. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. Lifespan Informatics & Neuroimaging Center University of Pennsylvania Philadelphia PA USA. Department of Psychology Yale University New Haven CT USA. Developmental Imaging Murdoch Children's Research Institute Melbourne Victoria Australia. Department of Medicine Monash University Melbourne Victoria Australia. UCL Great Ormond Street Institute for Child Health London UK. Weill Cornell Institute of Geriatric Psychiatry Department of Psychiatry Weill Cornell Medicine New York USA. Department of Pediatrics University of Toronto Toronto Canada. Holland Bloorview Kids Rehabilitation Hospital Toronto Canada. The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroInformation University of Electronic Science and Technology of China Chengdu China. University of Pinar del Río "Hermanos Saiz Montes de Oca" Pinar del Río Cuba. MRC Cognition and Brain Sciences Unit University of Cambridge Cambridge UK. Autism Research Centre Department of Psychiatry University of Cambridge Cambridge UK. Department of Psychology School of Philosophy Psychology and Language Sciences University of Edinburgh Edinburgh UK. Queen's

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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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