Zeitschriftenartikel zum Thema „Echocardiography segmentation“
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Liao, Minqi, Yifan Lian, Yongzhao Yao, Lihua Chen, Fei Gao, Long Xu, Xin Huang, Xinxing Feng und Suxia Guo. „Left Ventricle Segmentation in Echocardiography with Transformer“. Diagnostics 13, Nr. 14 (13.07.2023): 2365. http://dx.doi.org/10.3390/diagnostics13142365.
Der volle Inhalt der QuelleHuang, Helin, Zhenyi Ge, Hairui Wang, Jing Wu, Chunqiang Hu, Nan Li, Xiaomei Wu und Cuizhen Pan. „Segmentation of Echocardiography Based on Deep Learning Model“. Electronics 11, Nr. 11 (27.05.2022): 1714. http://dx.doi.org/10.3390/electronics11111714.
Der volle Inhalt der QuelleOno, 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, Nr. 5 (06.05.2022): 1082. http://dx.doi.org/10.3390/biomedicines10051082.
Der volle Inhalt der QuelleChen, Tongwaner, Menghua Xia, Yi Huang, Jing Jiao und Yuanyuan Wang. „Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation“. Sensors 23, Nr. 3 (28.01.2023): 1479. http://dx.doi.org/10.3390/s23031479.
Der volle Inhalt der QuelleWilczewska, Aleksandra, Szymon Cygan und Jakub Żmigrodzki. „Segmentation Enhanced Elastic Image Registration for 2D Speckle Tracking Echocardiography—Performance Study In Silico“. Ultrasonic Imaging 44, Nr. 1 (Januar 2022): 39–54. http://dx.doi.org/10.1177/01617346211068812.
Der volle Inhalt der QuelleTuncay, V., N. Prakken, P. M. A. van Ooijen, R. P. J. Budde, T. Leiner und 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.
Der volle Inhalt der QuelleEl rai, Marwa Chendeb, Muna Darweesh und Mina Al-Saad. „Semi-Supervised Segmentation of Echocardiography Videos Using Graph Signal Processing“. Electronics 11, Nr. 21 (26.10.2022): 3462. http://dx.doi.org/10.3390/electronics11213462.
Der volle Inhalt der QuelleHuang, Jun, Aiyue Huang, Ruqin Xu, Musheng Wu, Peng Wang und Qing Wang. „Automatic Segmentation and Assessment of Valvular Regurgitations with Color Doppler Echocardiography Images: A VABC-UNet-Based Framework“. Bioengineering 10, Nr. 11 (16.11.2023): 1319. http://dx.doi.org/10.3390/bioengineering10111319.
Der volle Inhalt der QuelleCai Ming, Huang Xiaoyang, Wang Boliang und Su Maolong. „Automatic Mitral Valve Leaflet Scallops Segmentation in Echocardiography“. International Journal of Advancements in Computing Technology 5, Nr. 8 (30.04.2013): 687–94. http://dx.doi.org/10.4156/ijact.vol5.issue8.78.
Der volle Inhalt der QuelleSkalski, Andrzej, und Paweł Turcza. „Heart Segmentation in Echo Images“. Metrology and Measurement Systems 18, Nr. 2 (01.01.2011): 305–14. http://dx.doi.org/10.2478/v10178-011-0012-y.
Der volle Inhalt der QuelleBarrile, V., F. Cotroneo, E. Genovese, E. Barrile und G. Bilotta. „AN AI SEGMENTER ON MEDICAL IMAGING FOR GEOMATICS APPLICATIONS CONSISTING OF A TWO-STATE PIPELINE, SNNS NETWORK AND WATERSHED ALGORITHM“. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W3-2023 (12.05.2023): 21–26. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-w3-2023-21-2023.
Der volle Inhalt der QuelleRachmatullah, M. N., Siti Nurmaini, A. I. Sapitri, A. Darmawahyuni, B. Tutuko und Firdaus Firdaus. „Convolutional neural network for semantic segmentation of fetal echocardiography based on four-chamber view“. Bulletin of Electrical Engineering and Informatics 10, Nr. 4 (01.08.2021): 1987–96. http://dx.doi.org/10.11591/eei.v10i4.3060.
Der volle Inhalt der QuelleCai, Junfeng, Xiahai Zhuang, Yuanyuan Nie, Zhe Luo und Lixu Gu. „Real-time aortic valve segmentation from transesophageal echocardiography sequence“. International Journal of Computer Assisted Radiology and Surgery 10, Nr. 4 (03.08.2014): 447–58. http://dx.doi.org/10.1007/s11548-014-1104-y.
Der volle Inhalt der QuelleValanrani, B. Arockia. „PREDICTING CARDIAC ISSUES FROM ECHOCARDIOGRAMS: A LITERATURE REVIEW USING DEEP LEARNING AND MACHINE LEARNING TECHNIQUES“. international journal of advanced research in computer science 15, Nr. 1 (20.02.2024): 5–13. http://dx.doi.org/10.26483/ijarcs.v15i1.7040.
Der volle Inhalt der QuelleDong, Suyu, Gongning Luo, Kuanquan Wang, Shaodong Cao, Qince Li und Henggui Zhang. „A Combined Fully Convolutional Networks and Deformable Model for Automatic Left Ventricle Segmentation Based on 3D Echocardiography“. BioMed Research International 2018 (10.09.2018): 1–16. http://dx.doi.org/10.1155/2018/5682365.
Der volle Inhalt der QuelleShoaib, Muhammad Ali, Joon Huang Chuah, Raza Ali, Samiappan Dhanalakshmi, Yan Chai Hum, Azira Khalil und Khin Wee Lai. „Fully Automatic Left Ventricle Segmentation Using Bilateral Lightweight Deep Neural Network“. Life 13, Nr. 1 (01.01.2023): 124. http://dx.doi.org/10.3390/life13010124.
Der volle Inhalt der QuelleTeng, Long, ZhongLiang Fu, Qian Ma, Yu Yao, Bing Zhang, Kai Zhu und Ping Li. „Interactive Echocardiography Translation Using Few-Shot GAN Transfer Learning“. Computational and Mathematical Methods in Medicine 2020 (19.03.2020): 1–9. http://dx.doi.org/10.1155/2020/1487035.
Der volle Inhalt der QuelleMortada, MHD Jafar, Selene Tomassini, Haidar Anbar, Micaela Morettini, Laura Burattini und Agnese Sbrollini. „Segmentation of Anatomical Structures of the Left Heart from Echocardiographic Images Using Deep Learning“. Diagnostics 13, Nr. 10 (09.05.2023): 1683. http://dx.doi.org/10.3390/diagnostics13101683.
Der volle Inhalt der QuelleNurmaini, Siti, Muhammad Naufal Rachmatullah, Ade Iriani Sapitri, Annisa Darmawahyuni, Bambang Tutuko, Firdaus Firdaus, Radiyati Umi Partan und Nuswil Bernolian. „Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection“. Sensors 21, Nr. 23 (30.11.2021): 8007. http://dx.doi.org/10.3390/s21238007.
Der volle Inhalt der QuelleYe, Zi, Yogan Jaya Kumar, Fengyan Song, Guanxi Li und Suyu Zhang. „Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation“. Life 13, Nr. 4 (18.04.2023): 1040. http://dx.doi.org/10.3390/life13041040.
Der volle Inhalt der QuelleKim, Dong Ok, Minsu Chae und HwaMin Lee. „Revolutionizing Echocardiography: A Comparative Study of Advanced AI Models for Precise Left Ventricular Segmentation“. International Journal on Advanced Science, Engineering and Information Technology 14, Nr. 3 (05.06.2024): 835–40. http://dx.doi.org/10.18517/ijaseit.14.3.18073.
Der volle Inhalt der QuelleStoean, Catalin, Nebojsa Bacanin, Wiesław Paja, Ruxandra Stoean, Dominic Iliescu, Ciprian Patru und Rodica Nagy. „Semantic segmentation of fetal heart components in second trimester echocardiography“. Procedia Computer Science 207 (2022): 3085–92. http://dx.doi.org/10.1016/j.procs.2022.09.366.
Der volle Inhalt der QuelleMazaheri, Samaneh, Puteri Suhaiza Binti Sulaiman, Rahmita Wirza, Mohd Zamrin Dimon, Fatima Khalid und Rohollah Moosavi Tayebi. „Segmentation Methods of Echocardiography Images for Left Ventricle Boundary Detection“. Journal of Computer Science 11, Nr. 9 (01.09.2015): 957–70. http://dx.doi.org/10.3844/jcssp.2015.957.970.
Der volle Inhalt der QuelleSigit, Riyanto, Calvin Alfa Roji, Tri Harsono und Son Kuswadi. „Improved echocardiography segmentation using active shape model and optical flow“. TELKOMNIKA (Telecommunication Computing Electronics and Control) 17, Nr. 2 (01.04.2019): 809. http://dx.doi.org/10.12928/telkomnika.v17i2.11821.
Der volle Inhalt der QuelleDanilov, V. V., I. P. Skirnevskiy und O. M. Gerget. „Segmentation of anatomical structures of the heart based on echocardiography“. Journal of Physics: Conference Series 803 (Januar 2017): 012031. http://dx.doi.org/10.1088/1742-6596/803/1/012031.
Der volle Inhalt der QuelleCorinzia, Luca, Fabian Laumer, Alessandro Candreva, Maurizio Taramasso, Francesco Maisano und Joachim M. Buhmann. „Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography“. Artificial Intelligence in Medicine 110 (November 2020): 101975. http://dx.doi.org/10.1016/j.artmed.2020.101975.
Der volle Inhalt der QuelleCelestin, B. E., S. P. Bagherzadeh, E. Santana, M. Frost, I. Mathias, A. J. Sweatt, R. Zamanian et al. „Echocardiography in Pulmonary Arterial Hypertension Using Deep Learning Segmentation Algorithms“. Journal of Heart and Lung Transplantation 43, Nr. 4 (April 2024): S410. http://dx.doi.org/10.1016/j.healun.2024.02.1312.
Der volle Inhalt der QuelleBalasubramani, Madankumar, Chih-Wei Sung, Mu-Yang Hsieh, Edward Pei-Chuan Huang, Jiann-Shing Shieh und Maysam F. Abbod. „Automated Left Ventricle Segmentation in Echocardiography Using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment“. Electronics 13, Nr. 13 (01.07.2024): 2587. http://dx.doi.org/10.3390/electronics13132587.
Der volle Inhalt der QuelleWu, Huisi, Jingyin Lin, Wende Xie und Jing Qin. „Super-efficient Echocardiography Video Segmentation via Proxy- and Kernel-Based Semi-supervised Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 3 (26.06.2023): 2803–11. http://dx.doi.org/10.1609/aaai.v37i3.25381.
Der volle Inhalt der QuelleZhuang, Zhemin, Pengcheng Jin, Alex Noel Joseph Raj, Ye Yuan und Shuxin Zhuang. „Automatic Segmentation of Left Ventricle in Echocardiography Based on YOLOv3 Model to Achieve Constraint and Positioning“. Computational and Mathematical Methods in Medicine 2021 (16.05.2021): 1–11. http://dx.doi.org/10.1155/2021/3772129.
Der volle Inhalt der QuelleShiri, M., H. Behnam, H. Yeganegi, Z. A. Sani und N. Nematollahi. „TRACKABLE-SPECKLE DETECTION USING A DUAL-PATH CONVOLUTIONAL NEURAL NETWORK FOR NODES SELECTION IN SPECKLE TRACKING ECHOCARDIOGRAPHY“. Asian Journal Of Medical Technology 2, Nr. 2 (05.08.2022): 33–54. http://dx.doi.org/10.32896/ajmedtech.v2n2.33-54.
Der volle Inhalt der QuelleHan, Guowei, Tianliang Jin, Li Zhang, Chen Guo, Hua Gui, Risu Na, Xuesong Wang und Haihua Bai. „Adoption of Compound Echocardiography under Artificial Intelligence Algorithm in Fetal Congenial Heart Disease Screening during Gestation“. Applied Bionics and Biomechanics 2022 (01.06.2022): 1–8. http://dx.doi.org/10.1155/2022/6410103.
Der volle Inhalt der QuelleAzizi, Fityan, Mgs M. Luthfi Ramadhan und Wisnu Jatmiko. „Encoder-Decoder with Atrous Spatial Pyramid Pooling for Left Ventricle Segmentation in Echocardiography“. Jurnal Ilmu Komputer dan Informasi 16, Nr. 2 (03.07.2023): 163–69. http://dx.doi.org/10.21609/jiki.v16i2.1165.
Der volle Inhalt der QuelleAmer, Alyaa, Xujiong Ye und Faraz Janan. „ResDUnet: A Deep Learning-Based Left Ventricle Segmentation Method for Echocardiography“. IEEE Access 9 (2021): 159755–63. http://dx.doi.org/10.1109/access.2021.3122256.
Der volle Inhalt der QuelleAndreassen, Borge Solli, Federico Veronesi, Olivier Gerard, Anne H. Schistad Solberg und Eigil Samset. „Mitral Annulus Segmentation Using Deep Learning in 3-D Transesophageal Echocardiography“. IEEE Journal of Biomedical and Health Informatics 24, Nr. 4 (April 2020): 994–1003. http://dx.doi.org/10.1109/jbhi.2019.2959430.
Der volle Inhalt der QuelleHu, Yujin, Bei Xia, Muyi Mao, Zelong Jin, Jie Du, Libao Guo, Alejandro F. Frangi, Baiying Lei und Tianfu Wang. „AIDAN: An Attention-Guided Dual-Path Network for Pediatric Echocardiography Segmentation“. IEEE Access 8 (2020): 29176–87. http://dx.doi.org/10.1109/access.2020.2971383.
Der volle Inhalt der QuelleBernard, Olivier, Johan G. Bosch, Brecht Heyde, Martino Alessandrini, Daniel Barbosa, Sorina Camarasu-Pop, Frederic Cervenansky et al. „Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography“. IEEE Transactions on Medical Imaging 35, Nr. 4 (April 2016): 967–77. http://dx.doi.org/10.1109/tmi.2015.2503890.
Der volle Inhalt der QuellePearlman, P. C., H. D. Tagare, B. A. Lin, A. J. Sinusas und J. S. Duncan. „Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictor“. Medical Image Analysis 16, Nr. 2 (Februar 2012): 351–60. http://dx.doi.org/10.1016/j.media.2011.09.002.
Der volle Inhalt der QuelleDong, Suyu, Gongning Luo, Clara Tam, Wei Wang, Kuanquan Wang, Shaodong Cao, Bo Chen, Henggui Zhang und Shuo Li. „Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography“. Medical Image Analysis 61 (April 2020): 101638. http://dx.doi.org/10.1016/j.media.2020.101638.
Der volle Inhalt der QuelleBersvendsen, Jørn, Fredrik Orderud, Øyvind Lie, Richard John Massey, Kristian Fosså, Raúl San José Estépar, Stig Urheim und Eigil Samset. „Semiautomated biventricular segmentation in three-dimensional echocardiography by coupled deformable surfaces“. Journal of Medical Imaging 4, Nr. 2 (24.05.2017): 024005. http://dx.doi.org/10.1117/1.jmi.4.2.024005.
Der volle Inhalt der QuelleShekhar, R., V. Zagrodsky und V. Walimbe. „3D Stress echocardiography: development of novel visualization, registration and segmentation algorithms“. International Congress Series 1268 (Juni 2004): 1072–77. http://dx.doi.org/10.1016/j.ics.2004.03.107.
Der volle Inhalt der QuelleBatool, Samana, Imtiaz Ahmad Taj und Mubeen Ghafoor. „Ejection Fraction Estimation from Echocardiograms Using Optimal Left Ventricle Feature Extraction Based on Clinical Methods“. Diagnostics 13, Nr. 13 (24.06.2023): 2155. http://dx.doi.org/10.3390/diagnostics13132155.
Der volle Inhalt der QuelleHuang, Mu-Shiang, Chi-Shiang Wang, Jung-Hsien Chiang, Ping-Yen Liu und Wei-Chuan Tsai. „Automated Recognition of Regional Wall Motion Abnormalities Through Deep Neural Network Interpretation of Transthoracic Echocardiography“. Circulation 142, Nr. 16 (20.10.2020): 1510–20. http://dx.doi.org/10.1161/circulationaha.120.047530.
Der volle Inhalt der QuelleMaman, S. Ghanbari, A. Shalbaf, H. Behnam, Z. Alizadeh Sani und M. Shojaei Fard. „FULLY AUTOMATIC SEGMENTATION OF LEFT VENTRICLE IN A SEQUENCE OF ECHOCARDIOGRAPHY IMAGES OF ONE CARDIAC CYCLE BY DYNAMIC DIRECTIONAL VECTOR FIELD CONVOLUTION (DDVFC) METHOD AND MANIFOLD LEARNING“. Biomedical Engineering: Applications, Basis and Communications 25, Nr. 02 (April 2013): 1350022. http://dx.doi.org/10.4015/s1016237213500221.
Der volle Inhalt der QuelleKang, Seungyoung, Sun Ju Kim, Hong Gi Ahn, Kyoung-Chul Cha und Sejung Yang. „Left ventricle segmentation in transesophageal echocardiography images using a deep neural network“. PLOS ONE 18, Nr. 1 (20.01.2023): e0280485. http://dx.doi.org/10.1371/journal.pone.0280485.
Der volle Inhalt der QuelleGinty, Olivia K., John M. Moore, Yuanwei Xu, Wenyao Xia, Satoru Fujii, Daniel Bainbridge, Terry M. Peters, Bob B. Kiaii und Michael W. A. Chu. „Dynamic Patient-Specific Three-Dimensional Simulation of Mitral Repair“. Innovations: Technology and Techniques in Cardiothoracic and Vascular Surgery 13, Nr. 1 (Januar 2018): 11–22. http://dx.doi.org/10.1097/imi.0000000000000463.
Der volle Inhalt der QuelleWahlang, Imayanmosha, Sk Mahmudul Hassan, Arnab Kumar Maji, Goutam Saha, Michal Jasinski, Zbigniew Leonowicz und Elzbieta Jasinska. „Classification of Valvular Regurgitation Using Echocardiography“. Applied Sciences 12, Nr. 20 (17.10.2022): 10461. http://dx.doi.org/10.3390/app122010461.
Der volle Inhalt der QuelleCui, Xiaoxiao, Pengfei Zhang, Yujun Li, Zhi Liu, Xiaoyan Xiao, Yang Zhang, Longkun Sun, Lizhen Cui, Guang Yang und Shuo Li. „MCAL: An Anatomical Knowledge Learning Model for Myocardial Segmentation in 2-D Echocardiography“. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 69, Nr. 4 (April 2022): 1277–87. http://dx.doi.org/10.1109/tuffc.2022.3151647.
Der volle Inhalt der QuelleLeclerc, Sarah, Erik Smistad, Joao Pedrosa, Andreas Ostvik, Frederic Cervenansky, Florian Espinosa, Torvald Espeland et al. „Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography“. IEEE Transactions on Medical Imaging 38, Nr. 9 (September 2019): 2198–210. http://dx.doi.org/10.1109/tmi.2019.2900516.
Der volle Inhalt der QuelleWu, H. S., D. Wang, L. Shi und C. M. Yu. „Automatic segmentation of left ventricle in 3D echocardiography using a level set approach“. International Journal of Cardiology 164, Nr. 2 (April 2013): S12—S13. http://dx.doi.org/10.1016/s0167-5273(13)70558-8.
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