CT-TEE Image Registration for Surgical Navigation of Congenital Heart Disease Based on a Cycle Adversarial Network
| العنوان: | CT-TEE Image Registration for Surgical Navigation of Congenital Heart Disease Based on a Cycle Adversarial Network |
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| المؤلفون: | Yunfei Lu, Li Xiao, Gou Shuiping, Linlin Chen, Huang Meiping, Jian Zhuang, Ningtao Liu, Bing Li, Jia-Wei Chen |
| المصدر: | Computational and Mathematical Methods in Medicine Computational and Mathematical Methods in Medicine, Vol 2020 (2020) |
| بيانات النشر: | Hindawi Limited, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Adult, Heart Defects, Congenital, Adolescent, Databases, Factual, Article Subject, Heart disease, Adversarial network, Computer science, Computer applications to medicine. Medical informatics, 0206 medical engineering, R858-859.7, Image registration, 02 engineering and technology, Surgical operation, Multimodal Imaging, General Biochemistry, Genetics and Molecular Biology, Visceral organ, Image Interpretation, Computer-Assisted, medicine, Humans, Computer vision, General Immunology and Microbiology, business.industry, Applied Mathematics, General Medicine, medicine.disease, 020601 biomedical engineering, Visualization, Surgery, Computer-Assisted, Modeling and Simulation, Neural Networks, Computer, Artificial intelligence, Tomography, Tomography, X-Ray Computed, Focus (optics), business, human activities, Echocardiography, Transesophageal, Research Article |
| الوصف: | Transesophageal echocardiography (TEE) has become an essential tool in interventional cardiologist’s daily toolbox which allows a continuous visualization of the movement of the visceral organ without trauma and the observation of the heartbeat in real time, due to the sensor’s location at the esophagus directly behind the heart and it becomes useful for navigation during the surgery. However, TEE images provide very limited data on clear anatomically cardiac structures. Instead, computed tomography (CT) images can provide anatomical information of cardiac structures, which can be used as guidance to interpret TEE images. In this paper, we will focus on how to transfer the anatomical information from CT images to TEE images via registration, which is quite challenging but significant to physicians and clinicians due to the extreme morphological deformation and different appearance between CT and TEE images of the same person. In this paper, we proposed a learning-based method to register cardiac CT images to TEE images. In the proposed method, to reduce the deformation between two images, we introduce the Cycle Generative Adversarial Network (CycleGAN) into our method simulating TEE-like images from CT images to reduce their appearance gap. Then, we perform nongrid registration to align TEE-like images with TEE images. The experimental results on both children’ and adults’ CT and TEE images show that our proposed method outperforms other compared methods. It is quite noted that reducing the appearance gap between CT and TEE images can benefit physicians and clinicians to get the anatomical information of ROIs in TEE images during the cardiac surgical operation. |
| وصف الملف: | text/xhtml |
| تدمد: | 1748-6718 1748-670X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71ed0f691da08566c217188000806aff https://doi.org/10.1155/2020/4942121 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....71ed0f691da08566c217188000806aff |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17486718 1748670X |
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