Literatura científica selecionada sobre o tema "Echocardiography segmentation"
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Artigos de revistas sobre o assunto "Echocardiography segmentation"
Liao, Minqi, Yifan Lian, Yongzhao Yao, Lihua Chen, Fei Gao, Long Xu, Xin Huang, Xinxing Feng e Suxia Guo. "Left Ventricle Segmentation in Echocardiography with Transformer". Diagnostics 13, n.º 14 (13 de julho de 2023): 2365. http://dx.doi.org/10.3390/diagnostics13142365.
Texto completo da fonteHuang, Helin, Zhenyi Ge, Hairui Wang, Jing Wu, Chunqiang Hu, Nan Li, Xiaomei Wu e Cuizhen Pan. "Segmentation of Echocardiography Based on Deep Learning Model". Electronics 11, n.º 11 (27 de maio de 2022): 1714. http://dx.doi.org/10.3390/electronics11111714.
Texto completo da fonteOno, Shunzaburo, Masaaki Komatsu, Akira Sakai, Hideki Arima, Mie Ochida, Rina Aoyama, Suguru Yasutomi et al. "Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning". Biomedicines 10, n.º 5 (6 de maio de 2022): 1082. http://dx.doi.org/10.3390/biomedicines10051082.
Texto completo da fonteChen, Tongwaner, Menghua Xia, Yi Huang, Jing Jiao e Yuanyuan Wang. "Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation". Sensors 23, n.º 3 (28 de janeiro de 2023): 1479. http://dx.doi.org/10.3390/s23031479.
Texto completo da fonteWilczewska, Aleksandra, Szymon Cygan e Jakub Żmigrodzki. "Segmentation Enhanced Elastic Image Registration for 2D Speckle Tracking Echocardiography—Performance Study In Silico". Ultrasonic Imaging 44, n.º 1 (janeiro de 2022): 39–54. http://dx.doi.org/10.1177/01617346211068812.
Texto completo da fonteTuncay, V., N. Prakken, P. M. A. van Ooijen, R. P. J. Budde, T. Leiner e M. Oudkerk. "Semiautomatic, Quantitative Measurement of Aortic Valve Area Using CTA: Validation and Comparison with Transthoracic Echocardiography". BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/648283.
Texto completo da fonteEl rai, Marwa Chendeb, Muna Darweesh e Mina Al-Saad. "Semi-Supervised Segmentation of Echocardiography Videos Using Graph Signal Processing". Electronics 11, n.º 21 (26 de outubro de 2022): 3462. http://dx.doi.org/10.3390/electronics11213462.
Texto completo da fonteHuang, Jun, Aiyue Huang, Ruqin Xu, Musheng Wu, Peng Wang e Qing Wang. "Automatic Segmentation and Assessment of Valvular Regurgitations with Color Doppler Echocardiography Images: A VABC-UNet-Based Framework". Bioengineering 10, n.º 11 (16 de novembro de 2023): 1319. http://dx.doi.org/10.3390/bioengineering10111319.
Texto completo da fonteCai Ming, Huang Xiaoyang, Wang Boliang e Su Maolong. "Automatic Mitral Valve Leaflet Scallops Segmentation in Echocardiography". International Journal of Advancements in Computing Technology 5, n.º 8 (30 de abril de 2013): 687–94. http://dx.doi.org/10.4156/ijact.vol5.issue8.78.
Texto completo da fonteSkalski, Andrzej, e Paweł Turcza. "Heart Segmentation in Echo Images". Metrology and Measurement Systems 18, n.º 2 (1 de janeiro de 2011): 305–14. http://dx.doi.org/10.2478/v10178-011-0012-y.
Texto completo da fonteTeses / dissertações sobre o assunto "Echocardiography segmentation"
Hang, Xiyi. "Compression and segmentation of three-dimensional echocardiography". Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1089835123.
Texto completo da fonteTitle from first page of PDF file. Document formatted into pages; contains xvii, 151 p.; also includes graphics (some col.). Includes bibliographical references (p. 145-151). Available online via OhioLINK's ETD Center
Dydenko, Igor Friboulet Denis. "Segmentation dynamique en échocardiographie ultrasonore radiofréquence ynamic segmentation in ultrasound radiofrequency echocardiography /". Villeurbanne : Doc'INSA, 2005. http://docinsa.insa-lyon.fr/these/pont.php?id=dydenko.
Texto completo da fonteThèse rédigée en anglais. Résumé en français en début de chaque chapitre. Titre provenant de l'écran-titre. Bibliogr. p. 216-232. Publications de l'auteur p. 214-215.
Zabair, Adeala Tuffail. "Segmentation of stress echocardiography sequences using a patient-specific prior". Thesis, University of Oxford, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.534181.
Texto completo da fonteVerhoek, Michael. "Fast segmentation of the LV myocardium in real-time 3D echocardiography". Thesis, University of Oxford, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566050.
Texto completo da fonteHovda, Sigve. "New Doppler-Based Imaging Methods in Echocardiography with Applications in Blood/Tissue Segmentation". Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1500.
Texto completo da fontePart 1: The bandwidth of the ultrasound Doppler signal is proposed as a classification function of blood and tissue signal in transthoracial echocardiography of the left ventricle. The new echocardiographic mode, Bandwidth Imaging, utilizes the difference in motion between tissue and blood. Specifically, Bandwidth Imaging is the absolute value of the normalized autocorrelation function with lag one. Bandwidth Imaging is therefore linearly dependent on the the square of the bandwidth estimated from the Doppler spectrum. A 2-tap Finite Impulse Response high-pass filter is used prior to autocorrelation calculation to account for the high level of DC clutter noise in the apical regions. Reasonable pulse strategies are discussed and several images of Bandwidth Imaging are included. An in vivo experiment is presented, where the apparent error rate of Bandwidth Imaging is compared with apparent error rate of Second-Harmonic Imaging on 15 healthy men. The apparent error rate is calculated from signal from all myocardial wall segments defined in \cite{Cer02}. The ground truth of the position of the myocardial wall segments is determined by manual tracing of endocardium in Second-Harmonic Imaging. A hypotheses test of Bandwidth Imaging having lower apparent error rate than
Second-Harmonic Imaging is proved for a p-value of 0.94 in 3 segments of end diastole and 1 segment in end systole on non averaged data. When data is averaged by a structural element of 5 radial, 3 lateral and 4 temporal samples, the numbers of segments are increased to 9 in end diastole and to 6 in end systole. These segments are mostly located in apical and anterior wall regions. Further, a global measure GM is defined as the proportion of misclassified area in the regions close to endocardium in an image. The hypothesis test of Second-Harmonic Imaging having lower GM than Bandwidth Imaging is proved for a p-value of 0.94 in the four-chamber view in end systole in any type of averaging. On the other side, the hypothesis test of Bandwidth Imaging having lower GM than Second-Harmonic Imaging is proved for a p-value of 0.94 in long-axis view in end diastole in any type of averaging. Moreover, if images are averaged by the above structural element the test indicates that Bandwidth Imaging has a lower apparent error rate than Second-Harmonic Imaging in all views and times (end diastole or end systole), except in four-chamber view in end systole. This experiment indicates that Bandwidth Imaging can supply additional information for automatic border detection routines on endocardium.
Part 2: Knowledge Based Imaging is suggested as a method to distinguish blood from tissue signal in transthoracial echocardiography. This method utilizes the maximum likelihood function to classify blood and tissue signal. Knowledge Based Imaging uses the same pulse strategy as Bandwidth Imaging, but is significantly more difficult to implement. Therefore, Knowledge Based Imaging and Bandwidth Imaging are compared with Fundamental Imaging by a computer simulation based on a parametric model of the signal. The rate apparent error rate is calculated in any reasonable tissue to blood signal ratio, tissue to white noise ratio and clutter to white noise ratio. Fundamental Imaging classifies well when tissue to blood signal ratio is high and tissue to white noise ratio is higher than clutter to white noise ratio. Knowledge Based Imaging classifies also well in this environment. In addition, Knowledge Based Imaging classifies well whenever blood to white noise ratio is above 30 dB. This is the case, even when clutter to white noise ratio is higher than tissue to white noise ratio and tissue to blood signal ratio is zero. Bandwidth Imaging performs similar to Knowledge Based Imaging, but blood to white noise ratio has to be 20 dB higher for a reasonable classification. Also the highpass filter coefficient prior to Bandwidth Imaging calculation is discussed by the simulations. Some images of different parameter settings of Knowledge Based Imaging are visually compared with Second-Harmonic Imaging, Fundamental Imaging and Bandwidth Imaging. Changing parameters of Knowledge Based Imaging can make the image look similar to both Bandwidth Imaging and Fundamental Imaging.
Icenogle, David A. "Development of virtual mitral valve leaflet models from three-dimensional echocardiography". Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/48994.
Texto completo da fonteWalimbe, Vivek S. "Interactive, quantitative 3D stress echocardiography and myocardial perfusion spect for improved diagnosis of coronary artery disease". Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1154710169.
Texto completo da fonteDindoyal, I. "Foetal echocardiographic segmentation". Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/20169/.
Texto completo da fonteBarbosa, Daniel. "Automated assessment of cardiac morphology and function : An integrated B-spline framework for real-time segmentation and tracking of the left ventricle". Thesis, Lyon, INSA, 2013. http://www.theses.fr/2013ISAL0111.
Texto completo da fonteThe fundamental goal of the present thesis was the development of automatic strategies for left ventricular (LV) segmentation and tracking in RT3DE data. Given the challenging nature of RT3DE data, classical computer vision algorithms often face complications when applied to ultrasound. Furthermore, the proposed solutions were formalized and built to respect the following requirements: they should allow (nearly) fully automatic analysis and their computational burden should be low, thus enabling real-time processing for optimal online clinical use. With this in mind, we have proposed a novel segmentation framework where the latest developments in level-set-based image segmentation algorithms could be straightforwardly integrated, while avoiding the heavy computational burden often associated with level-set algorithms. Furthermore, a strong validation component was included in order to assess the performance of the proposed algorithms in realistic scenarios comprising clinical data. First, the performance of the developed tools was evaluated from a global perspective, focusing on its use in clinical daily practice. Secondly, also the spatial accuracy of the estimated left ventricular boundaries was assessed. As a final step, we aimed at the integration of the developed methods in an in-house developed software suite used for research purposes. This included user-friendly solutions for efficient daily use, namely user interactive tools to adjust the segmented left ventricular boundaries
Souza, André Fernando Lourenço de. "Abordagens para a segmentação de coronárias em ecocardiografia". Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-20102010-123221/.
Texto completo da fonteThe echocardiography is the imaging technique that remains most promising, noninvasive, no ionizing radiation and inexpensive to assess heart conditions. On the other hand, is considerably affected by noises, such as speckle, that are very difficult to be filtered. That is why it is necessary to make the right choice of filter and segmentation method to obtain the best results on image segmentation. The goal was evaluate this filter and segmentation method combination. For that, it was developed a segmentation system, to help the assessment. Two filters were implemented to mitigate the effect of speckle noise Linear Scaling Mean Variance (LSMV) and the filter presented by Chitwong - to be tested in simulated images. We simulated 60 images, with size 300 by 300 pixels, 3 models, 4 thicknesses and 5 different levels of contrast, all with speckle noise. In addition, tests were made with a combination of filters. Furthermore, it was implemented a Fuzzy Connectedness algorithm and an evaluation system, following the criteria described by Loizou, which makes the true positives (TP) and false positives (FP) counting. It was found that the LSMV filter is the best option for Fuzzy Connectedness. We obtained rates of TP and FP of 95% and 5% using LSMV, and accuracy of 95%. Using high contrast noisy images, without filtering, we obtained the accuracy in order of 60%.
Livros sobre o assunto "Echocardiography segmentation"
Lancellotti, Patrizio, e Bernard Cosyns. Assessment of the Left Ventricular Systolic Function. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198713623.003.0004.
Texto completo da fonteCapítulos de livros sobre o assunto "Echocardiography segmentation"
Picano, Eugenio. "Segmentation of the Left Ventricle". In Stress Echocardiography, 91–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-76466-3_7.
Texto completo da fontePicano, Eugenio. "Segmentation of the Left Ventricle". In Stress Echocardiography, 46–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-13061-2_5.
Texto completo da fontePicano, Eugenio. "Segmentation of the Left Ventricle". In Stress Echocardiography, 51–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-10090-5_6.
Texto completo da fontePicano, Eugenio. "Segmentation of the Left Ventricle". In Stress Echocardiography, 57–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05096-5_6.
Texto completo da fontePicano, Eugenio. "Segmentation of the Left Ventricle". In Stress Echocardiography, 61–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-02979-4_6.
Texto completo da fonteBadano, Luigi P., e Eugenio Picano. "Standardized Myocardial Segmentation of the Left Ventricle". In Stress Echocardiography, 105–19. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20958-6_7.
Texto completo da fonteFeng, Zishun, Joseph A. Sivak e Ashok K. Krishnamurthy. "Improving Echocardiography Segmentation by Polar Transformation". In Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers, 133–42. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23443-9_13.
Texto completo da fonteSaeed, Mohamed, Rand Muhtaseb e Mohammad Yaqub. "Contrastive Pretraining for Echocardiography Segmentation with Limited Data". In Medical Image Understanding and Analysis, 680–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12053-4_50.
Texto completo da fonteGuo, Libao, Yujin Hu, Baiying Lei, Jie Du, Muyi Mao, Zelong Jin, Bei Xia e Tianfu Wang. "Dual Network Generative Adversarial Networks for Pediatric Echocardiography Segmentation". In Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis, 113–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32875-7_13.
Texto completo da fonteMartin, Sébastien, Vincent Daanen, Olivier Chavanon e Jocelyne Troccaz. "Fast Segmentation of the Mitral Valve Leaflet in Echocardiography". In Computer Vision Approaches to Medical Image Analysis, 225–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11889762_20.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Echocardiography segmentation"
Mazaheri, Samaneh, Puteri Suhaiza Binti Sulaiman, Rahmita Wirza, Fatimah Khalid, Suhaini Kadiman, Mohd Zamrin Dimon e Rohollah Moosavi Tayebi. "Echocardiography Image Segmentation: A Survey". In 2013 International Conference on Advanced Computer Science Applications and Technologies (ACSAT). IEEE, 2013. http://dx.doi.org/10.1109/acsat.2013.71.
Texto completo da fonteTran, Tung, Joshua V. Stough, Xiaoyan Zhang e Christopher M. Haggerty. "Bayesian Optimization of 2D Echocardiography Segmentation". In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9433868.
Texto completo da fonteVyas, Saurabh, Ryan Mukherjee, Federico Sosa e Philippe Burlina. "Endocardium segmentation in 3D Transesophageal Echocardiography". In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556411.
Texto completo da fonteChechani, Shubham, Rahul Suresh e Kedar A. Patwardhan. "Aortic root segmentation in 4D transesophageal echocardiography". In Computer-Aided Diagnosis, editado por Kensaku Mori e Nicholas Petrick. SPIE, 2018. http://dx.doi.org/10.1117/12.2293056.
Texto completo da fonteMonkam, Patrice, Songbai Jin e Wenkai Lu. "Multi-task learning framework for echocardiography segmentation". In 2022 IEEE International Ultrasonics Symposium (IUS). IEEE, 2022. http://dx.doi.org/10.1109/ius54386.2022.9957223.
Texto completo da fonteNakphu, Nonthaporn, Dyah Ekashanti Octorina Dewi, Muhammad Qurhanul Rizqie, Eko Supriyanto, Ahmad 'Athif Mohd Faudzi, Dolwin Ching Ching Kho, Suhaini Kadiman e Panrasee Rittipravat. "Apical four-chamber echocardiography segmentation using Marker-controlled Watershed segmentation". In 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2014. http://dx.doi.org/10.1109/iecbes.2014.7047583.
Texto completo da fonteMilićević, Bogdan, Miljan Milošević, Mina Vasković Jovanović, Vladimir Milovanović, Nenad Filipovic e Miloš Kojić. "Overview of Left Ventricular Segmentation in Ultrasound Images". In 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.359m.
Texto completo da fonteCarnahan, Patrick, Elvis Chen e Terry Peters. "From 4D Transesophageal Echocardiography to Patient Specific Mitral Valve Models". In THE HAMLYN SYMPOSIUM ON MEDICAL ROBOTICS. The Hamlyn Centre, Imperial College London London, UK, 2023. http://dx.doi.org/10.31256/hsmr2023.77.
Texto completo da fonteGalluzzo, F., D. Barbosa, H. Houle, N. Speciale, D. Friboulet, J. D'hooge e O. Bernard. "A GPU level-set segmentation framework for 3D Echocardiography". In 2012 IEEE International Ultrasonics Symposium. IEEE, 2012. http://dx.doi.org/10.1109/ultsym.2012.0661.
Texto completo da fonteChen, Yida, Xiaoyan Zhang, Christopher M. Haggerty e Joshua V. Stough. "Assessing the generalizability of temporally coherent echocardiography video segmentation". In Image Processing, editado por Bennett A. Landman e Ivana Išgum. SPIE, 2021. http://dx.doi.org/10.1117/12.2580874.
Texto completo da fonte