Abstract
Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of medical images through internal/external communication systems, have resulted in a recent surge of significant interest in "Radiomics". Radiomics is an emerging and relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic models, and is expected to become a critical component for integration of image-derived information for personalized treatment in the near future. The conventional Radiomics workflow is typically based on extracting pre-designed features (also referred to as hand-crafted or engineered features) from a segmented region of interest. Nevertheless, recent advancements in deep learning have caused trends towards deep learning-based Radiomics (also referred to as discovery Radiomics). Capitalizing on the advantageous of these two approaches, there are also hybrid solutions developed to exploit the potentials of multiple data sources. Considering the variety of approaches to Radiomics, further improvements require a comprehensive and integrated sketch, which is the goal of this article. This manuscript provides a unique interdisciplinary perspective on Radiomics by discussing state-of-the-art signal processing solutions in the context of cancer Radiomics.
Abstract (translated)
信号处理和机器学习的最新进展加上医院中电子病历记录的发展以及通过内部/外部通信系统提供的大量医学图像，最近引起了对“放射学”的极大兴趣。 Radiomics是一个新兴的相对较新的研究领域，它指的是从医学图像中提取半定量和/或定量特征，目的是开发预测模型和/或预测模型，并有望成为图像整合的关键组成部分。在不久的将来获得个性化治疗的信息。传统的Radiomics工作流程通常基于从感兴趣的分段区域提取预先设计的特征（也称为手工制作或工程特征）。尽管如此，深度学习的最新进展已经引起了基于深度学习的放射性组学（也称为发现放射学）的趋势。利用这两种方法的优势，还开发了混合解决方案以利用多个数据源的潜力。考虑到Radiomics的各种方法，进一步的改进需要一个全面和综合的草图，这是本文的目标。本手稿通过讨论癌症放射组学背景下最先进的信号处理解决方案，为放射组学提供了独特的跨学科视角。
URL
https://arxiv.org/abs/1808.07954
