磁共振成像
计算机断层摄影术
实时核磁共振成像
计算机科学
分割
放射科
人工智能
翻译(生物学)
断层摄影术
医学
计算机视觉
生物化学
化学
信使核糖核酸
基因
作者
Khoa Tan Truong,Thai Hoang Le
标识
DOI:10.1109/kse56063.2022.9953622
摘要
The detection and treatment of cancer and other disorders depend on the use of magnetic resonance imaging (MRI) and computed tomography (CT) scans. Compared to CT scan, MRI scans provide sharper pictures. An MRI is preferable to an X-ray or CT scan when the doctor needs to observe the soft tissues. Besides, MRI scans of organs and soft tissues, such as damaged ligaments and herniated discs, can be more accurate than CT imaging. However, capturing MRI typically takes longer than CT. Furthermore, MRI is substantially more expensive than CT because it requires more sophisticated current equipment. As a result, it is challenging to gather MRI scans to help with the medical image segmentation training issue. To address the aforementioned issue, we suggest using a deep learning network (TarGAN) to reconstruct MRI from CT scans. These created MRI images can then be used to enrich training data for MRI images segmentation issues.
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