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arXiv 2011.00454eess.IVcs.CV

Dynamic radiomics: a new methodology to extract quantitative time-related features from tomographic images

Northeastern University(东北大学);Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education(教育部医学影像智能计算重点实验室);Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University(东北大学流程工业综合自动化国家重点实验室);the First Affiliated Hospital of China Medical University(中国医科大学附属第一医院);Shenzhen College of Advanced Technology, University of the Chinese Academy of Sciences(中国科学院大学深圳先进技术学院);Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University(中国医科大学肿瘤医院辽宁省肿瘤医院)

Fengying Che, Ruichuan Shi, Jian Wu, Haoran Li, Shuqin Li, Weixing Chen, Hao Zhang, Zhi Li, Xiaoyu Cui

英文摘要:

The feature extraction methods of radiomics are mainly based on static tomographic images at a certain moment, while the occurrence and development of disease is a dynamic process that cannot be fully reflected by only static characteristics. This study proposes a new dynamic radiomics feature extraction workflow that uses time-dependent tomographic images of the same patient, focuses on the changes in image features over time, and then quantifies them as new dynamic features for diagnostic or prognostic evaluation. We first define the mathematical paradigm of dynamic radiomics and introduce three specific methods that can describe the transformation process of features over time. Three different clinical problems are used to validate the performance of the proposed dynamic feature with conventional 2D and 3D static features.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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