Recent advances in large scale whole body MRI image analysis: Imiomics

R. Strand, Simon Ekström, Eva Breznik, T. Sjöholm, M. Pilia, L. Lind, F. Malmberg, H. Ahlström, J. Kullberg
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引用次数: 1

Abstract

Due to the massive amount of medical image data being made available, in research and clinical work, computer-aided tools are valuable and have a great potential for a sustainable work situation for physicians and for generating disease understanding. High-end methods in the present era of big data and artifical intelligence are designed to efficiently find patterns in large scale image data. The amount of data is today often too big to be parsed by human experts, and computer-assisted methods often perform at least as well as human experts on well-defined problems where it is possible to quantify performance by a loss function. This paper gives an overview of a computer-assisted method, Imiomics. Imiomics enables statistical analyses of relations between whole body image image data in large cohorts and other non-imaging data, at an unprecedented spatial resolution. Its usefulness in medicine is illustrated by a number of medical applications, and some aspects of technical development that enable the analysis is also presented. We conclude that computer-assisted methods, such as Imiomics, are essential for efficient processing of the huge amount of data in today's medical research and, to some extent, clinical practice.
大规模全身MRI图像分析的最新进展:模拟组学
由于大量的医学图像数据被提供,在研究和临床工作中,计算机辅助工具是有价值的,并且对于医生的可持续工作环境和产生疾病理解具有巨大的潜力。在当今大数据和人工智能时代,高端方法旨在高效地从大规模图像数据中发现模式。今天的数据量通常太大,无法由人类专家解析,而计算机辅助方法在定义明确的问题上的表现通常至少与人类专家一样好,这些问题可以通过损失函数来量化性能。本文概述了一种计算机辅助方法——模拟组学。Imiomics能够以前所未有的空间分辨率统计分析大型队列中的全身图像数据与其他非成像数据之间的关系。它在医学上的有用性通过若干医学应用加以说明,并且还介绍了使分析成为可能的技术发展的一些方面。我们得出的结论是,计算机辅助方法,如模拟组学,对于有效处理当今医学研究中的大量数据至关重要,在某种程度上,对于临床实践也是如此。
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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