Automated left ventricular segmentation from cardiac magnetic resonance images via adversarial learning with multi-stage pose estimation network and co-discriminator |
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| Institution: | 1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China, 518060;2. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China, 518060;1. Department of Neurosurgery, University of California, Los Angeles, United States;2. Department of Radiology, University of California, Los Angeles, United States;1. Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, 11377,NY, USA;2. Electrical and Computer Engineering Department, Northeastern University, Boston, 02115, MA, USA;3. Caliber Imaging and Diagnostics, Rochester, Rochester, 14623, NY, USA;4. Department of Pathology at SUNY Downstate Medical Center, New York, 11203, NY, USA;5. SkinMedical Research Diagnostics, P.L.L.C., Dobbs Ferry, 10522, NY, USA;6. University of Modena and Reggio Emilia, Reggio Emilia, Italy;1. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, China;2. School of Computer Science and Technology, Anhui University, China;3. Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Anhui, China;4. School of Health Science, Western University, London, ON N6A 3K7, Canada;5. Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada;1. Centre for Medical Image Computing, University College London, London, United Kingdom;2. Université Côte dAzur, Inria, Epione Team, 06902 Sophia Antipolis, France;3. School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom;4. NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and the Institute of Ophthalmology, University College London, London, United Kingdom;1. Faculty of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, China;2. Department of General Surgery, Huaqiao University Affiliated Strait Hospital, Quanzhou, Fujian 362000, China;3. Key Laboratory of Intelligent Computing and Information Processing, Fujian Province University, Quanzhou 362000, China;4. Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou 362000, China;5. Department of Diagnostic Radiology, Huaqiao University Affiliated Strait Hospital, Quanzhou, Fujian 362000, China;1. Department of Cardiology, the Fifth Affiliated Hospital of Southern Medical University, Guangzhou, China;2. Department of Cardiac Surgery, Kunming Medical University Affiliated Yan''an Hospital, Kunming, China;3. Department of Interventional Radiology, Affiliated Hospital of Guilin Medical University, Guilin, China;4. Department of Heart Failure, Kunming Medical University Affiliated Yan''an Hospital, Kunming, China |
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| Abstract: | Left ventricular (LV) segmentation is essential for the early diagnosis of cardiovascular diseases, which has been reported as the leading cause of death all over the world. However, automated LV segmentation from cardiac magnetic resonance images (CMRI) using the traditional convolutional neural networks (CNNs) is still a challenging task due to the limited labeled CMRI data and low tolerances to irregular scales, shapes and deformations of LV. In this paper, we propose an automated LV segmentation method based on adversarial learning by integrating a multi-stage pose estimation network (MSPN) and a co-discrimination network. Different from existing CNNs, we use a MSPN with multi-scale dilated convolution (MDC) modules to enhance the ranges of receptive field for deep feature extraction. To fully utilize both labeled and unlabeled CMRI data, we propose a novel generative adversarial network (GAN) framework for LV segmentation by combining MSPN with co-discrimination networks. Specifically, the labeled CMRI are first used to initialize our segmentation network (MSPN) and co-discrimination network. Our GAN training includes two different kinds of epochs fed with both labeled and unlabeled CMRI data alternatively, which are different from the traditional CNNs only relied on the limited labeled samples to train the segmentation networks. As both ground truth and unlabeled samples are involved in guiding training, our method not only can converge faster but also obtain a better performance in LV segmentation. Our method is evaluated using MICCAI 2009 and 2017 challenge databases. Experimental results show that our method has obtained promising performance in LV segmentation, which also outperforms the state-of-the-art methods in terms of LV segmentation accuracy from the comparison results. |
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