4 results on '"Vesal, Sulaiman"'
Search Results
2. Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge
- Author
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Zhuang, Xiahai, primary, Xu, Jiahang, additional, Luo, Xinzhe, additional, Chen, Chen, additional, Ouyang, Cheng, additional, Rueckert, Daniel, additional, Campello, Victor M., additional, Lekadir, Karim, additional, Vesal, Sulaiman, additional, RaviKumar, Nishant, additional, Liu, Yashu, additional, Luo, Gongning, additional, Chen, Jingkun, additional, Li, Hongwei, additional, Ly, Buntheng, additional, Sermesant, Maxime, additional, Roth, Holger, additional, Zhu, Wentao, additional, Wang, Jiexiang, additional, Ding, Xinghao, additional, Wang, Xinyue, additional, Yang, Sen, additional, and Li, Lei, additional
- Published
- 2022
- Full Text
- View/download PDF
3. A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging
- Author
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Xiong, Zhaohan, primary, Xia, Qing, additional, Hu, Zhiqiang, additional, Huang, Ning, additional, Bian, Cheng, additional, Zheng, Yefeng, additional, Vesal, Sulaiman, additional, Ravikumar, Nishant, additional, Maier, Andreas, additional, Yang, Xin, additional, Heng, Pheng-Ann, additional, Ni, Dong, additional, Li, Caizi, additional, Tong, Qianqian, additional, Si, Weixin, additional, Puybareau, Elodie, additional, Khoudli, Younes, additional, Géraud, Thierry, additional, Chen, Chen, additional, Bai, Wenjia, additional, Rueckert, Daniel, additional, Xu, Lingchao, additional, Zhuang, Xiahai, additional, Luo, Xinzhe, additional, Jia, Shuman, additional, Sermesant, Maxime, additional, Liu, Yashu, additional, Wang, Kuanquan, additional, Borra, Davide, additional, Masci, Alessandro, additional, Corsi, Cristiana, additional, de Vente, Coen, additional, Veta, Mitko, additional, Karim, Rashed, additional, Preetha, Chandrakanth Jayachandran, additional, Engelhardt, Sandy, additional, Qiao, Menyun, additional, Wang, Yuanyuan, additional, Tao, Qian, additional, Nuñez-Garcia, Marta, additional, Camara, Oscar, additional, Savioli, Nicolo, additional, Lamata, Pablo, additional, and Zhao, Jichao, additional
- Published
- 2021
- Full Text
- View/download PDF
4. A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging
- Author
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Yashu Liu, Davide Borra, Sandy Engelhardt, Daniel Rueckert, Pheng-Ann Heng, Caizi Li, Elodie Puybareau, Xin Yang, Chandrakanth Jayachandran Preetha, Weixin Si, Menyun Qiao, Jichao Zhao, Maxime Sermesant, Ning Huang, Mitko Veta, Kuanquan Wang, Thierry Géraud, Younes Khoudli, Zhiqiang Hu, Coen de Vente, Nishant Ravikumar, Nicoló Savioli, Alessandro Masci, Dong Ni, Xiahai Zhuang, Qianqian Tong, Wenjia Bai, Yefeng Zheng, Oscar Camara, Shuman Jia, Xinzhe Luo, Chen Chen, Yuanyuan Wang, Qian Tao, Zhaohan Xiong, Cheng Bian, Cristiana Corsi, Qing Xia, Rashed Karim, Sulaiman Vesal, Marta Nuñez-Garcia, Andreas Maier, Lingchao Xu, Pablo Lamata, Engineering & Physical Science Research Council (EPSRC), Xiong, Zhaohan, Xia, Qing, Hu, Zhiqiang, Huang, Ning, Bian, Cheng, Zheng, Yefeng, Vesal, Sulaiman, Ravikumar, Nishant, Maier, Andrea, Yang, Xin, Heng, Pheng-Ann, Ni, Dong, Li, Caizi, Tong, Qianqian, Si, Weixin, Puybareau, Elodie, Khoudli, Youne, Géraud, Thierry, Chen, Chen, Bai, Wenjia, Rueckert, Daniel, Xu, Lingchao, Zhuang, Xiahai, Luo, Xinzhe, Jia, Shuman, Sermesant, Maxime, Liu, Yashu, Wang, Kuanquan, Borra, Davide, Masci, Alessandro, Corsi, Cristiana, de Vente, Coen, Veta, Mitko, Karim, Rashed, Preetha, Chandrakanth Jayachandran, Engelhardt, Sandy, Qiao, Menyun, Wang, Yuanyuan, Tao, Qian, Nuñez-Garcia, Marta, Camara, Oscar, Savioli, Nicolo, Lamata, Pablo, Zhao, Jichao, Medical Image Analysis, and EAISI Health
- Subjects
Technology ,Computer science ,cs.LG ,Gadolinium ,Late gadolinium-enhanced magnetic resonance imaging ,Convolutional neural network ,Computer Science, Artificial Intelligence ,09 Engineering ,Field (computer science) ,030218 nuclear medicine & medical imaging ,Engineering ,0302 clinical medicine ,Segmentation ,cs.CV ,11 Medical and Health Sciences ,Image segmentation ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,Radiology, Nuclear Medicine & Medical Imaging ,Heart Atria/diagnostic imaging ,stat.ML ,Magnetic Resonance Imaging ,Computer Graphics and Computer-Aided Design ,Nuclear Medicine & Medical Imaging ,Benchmarking ,Left atrium ,Benchmark (computing) ,Computer Science, Interdisciplinary Applications ,Convolutional neural networks ,Computer Vision and Pattern Recognition ,Life Sciences & Biomedicine ,Algorithm ,Algorithms ,MRI ,Health Informatics ,03 medical and health sciences ,Market segmentation ,Cardiac magnetic resonance imaging ,Medical imaging ,medicine ,Humans ,AUTOMATIC SEGMENTATION ,Radiology, Nuclear Medicine and imaging ,Heart Atria ,cardiovascular diseases ,Engineering, Biomedical ,Science & Technology ,Computer Science ,eess.IV ,030217 neurology & neurosurgery - Abstract
Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world's largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community. (C) 2020 Elsevier B.V. All rights reserved.
- Published
- 2021
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