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MRI图像分析方法与应用:以脑肿瘤为例的算法视角

MRI image analysis methods and applications: an algorithmic perspective using brain tumors as an exemplar.

作者信息

Vadmal Vachan, Junno Grant, Badve Chaitra, Huang William, Waite Kristin A, Barnholtz-Sloan Jill S

机构信息

Department of Population Health and Quantitative Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio.

Department of Radiology, University Hospitals Health System (UHHS), Cleveland, Ohio.

出版信息

Neurooncol Adv. 2020 Apr 14;2(1):vdaa049. doi: 10.1093/noajnl/vdaa049. eCollection 2020 Jan-Dec.

Abstract

The use of magnetic resonance imaging (MRI) in healthcare and the emergence of radiology as a practice are both relatively new compared with the classical specialties in medicine. Having its naissance in the 1970s and later adoption in the 1980s, the use of MRI has grown exponentially, consequently engendering exciting new areas of research. One such development is the use of computational techniques to analyze MRI images much like the way a radiologist would. With the advent of affordable, powerful computing hardware and parallel developments in computer vision, MRI image analysis has also witnessed unprecedented growth. Due to the interdisciplinary and complex nature of this subfield, it is important to survey the current landscape and examine the current approaches for analysis and trend trends moving forward.

摘要

与医学中的传统专业相比,磁共振成像(MRI)在医疗保健中的应用以及放射学作为一种实践的出现都相对较新。MRI的应用始于20世纪70年代,并在80年代得到进一步应用,其使用量呈指数级增长,从而催生了令人兴奋的新研究领域。其中一项发展是使用计算技术来分析MRI图像,就像放射科医生那样。随着价格亲民、功能强大的计算硬件的出现以及计算机视觉的并行发展,MRI图像分析也经历了前所未有的增长。由于这个子领域具有跨学科和复杂性,审视当前的状况并研究当前的分析方法以及未来的发展趋势非常重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e67/7236385/5cfebcd0bd51/vdaa049f0001.jpg

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