深度学习在MRI和CT医学图像处理中的应用综述

Ahliddin Shomirov, Jing Zhang
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引用次数: 1

摘要

医学形象是所有以改善健康为主要目标的组织、机构和资源的集合。医疗数据的广泛增长增加了机器学习和深度学习在医疗保健领域的效用。目前,使用深度训练来处理医学图像受到了特别的关注。近年来,在人工智能的帮助下,医疗器械得到了迅速发展,并被广泛应用于医学图像处理。人工智能是医学成像处理的众多来源,如x射线、计算机断层扫描(CT)和磁共振成像(MRI)。CT和MRI图像处理任务具有较高的计算时间要求和计算速度。目前,计算机技术在神经科学领域发展的一个重要方向是医学图像和数字图像的处理,用于提高图像质量、恢复受损图像、识别单个元素和诊断各种疾病。在本文中,我们简要回顾了深度学习在CT和MRI医学图像处理中的进展和挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Overview of Deep Learning in MRI and CT Medical Image Processing
The medical image is a set of all organizations, institutions, and resources whose primary goal is to improve health. The extensive growth of medical data increases the utility of machine learning and deep learning in the healthcare domains. Nowadays, the use of in-depth training to process medical images has received particular attention. In recent years, medical instruments have developed rapidly with the help of artificial intelligence and are widely used to process medical images. Artificial intelligence is numerous sources of medical imaging processing such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). CT and MRI image processing tasks with a high computation time requirement and computation speed. Nowadays, one of the most critical trends in the development of computer technology in neuroscience is the processing of medical images and digital images, which are used to improve image quality, restore damaged images, identify individual elements and diagnose various diseases. In this paper, we briefly review the progress and challenges associated with in-deep learning in the processing of CT and MRI medical images.
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