Importance of Deep Learning Models in the Medical Imaging Field

Preeti Sharma, Devershi Pallavi Bhatt
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引用次数: 5

Abstract

Medical imaging applications like MRI, CT scan, x-ray, PET, ultrasound, etc. provide health experts fast and comprehensive information of the internal organs and tissues of the human body. MRI of the brain is used to get inside information of any sort of brain injury, tumor, stroke, or wound in a blood vessel. The complex structure of the brain makes it a challenging responsibility for the researcher to design a model to precisely segment the brain region from the skull and to find any abnormality in the tissue. This chapter helps to understand the importance of deep learning to perform segmentation on MRI (magnetic resonance imaging) scans of the brain by reviewing previous studies and also presents brief knowledge of different brain imaging techniques, digital image segmentation techniques, and deep learning.
深度学习模型在医学影像领域的重要性
MRI、CT扫描、x线、PET、超声等医学成像应用为健康专家提供了快速、全面的人体内部器官和组织信息。大脑的核磁共振成像用于获取任何类型的脑损伤、肿瘤、中风或血管损伤的内部信息。大脑的复杂结构使得研究人员设计一个模型来精确地从头骨中分割大脑区域并发现组织中的任何异常,这是一项具有挑战性的任务。本章通过回顾以往的研究,帮助理解深度学习对大脑MRI(磁共振成像)扫描进行分割的重要性,并简要介绍了不同的脑成像技术、数字图像分割技术和深度学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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