26 January 2017 Radiomics-based quantitative biomarker discovery: development of a robust image processing infrastructure
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Proceedings Volume 10160, 12th International Symposium on Medical Information Processing and Analysis; 1016017 (2017) https://doi.org/10.1117/12.2256829
Event: 12th International Symposium on Medical Information Processing and Analysis, 2016, Tandil, Argentina
Abstract
Radiomics workflows are high-throughput disease descriptive or predictive tools that extract mineable quantitative data of pathological phenotypes from standard-of-care grayscale images using advanced image processing algorithms. The success of these workflows rely on establishing large image datasets from which diverse disease descriptors can be extracted, with the expectation that large numbers may be able to overcome some of the inherent heterogeneities inherent in standard-of-care medical imaging workflows. Here, we present such a radiomics platform which relies on a combination of existing standard-of-care imaging clinical and research software as well as custom written code. The key components of the workflow include a file organization schema for centralized data storage, deployment of image registration strategies, and frontend GUI design for ease of use by the clinical researcher, all of which increase the transparency, flexibility, and portability of our radiomics platform. Widespread establishment of such radiomics platform can greatly revolutionize radiomics research and aid in successful translation into clinical decision support systems.

Presented are three preliminary studies completed using our proposed radiomics research workflow to investigate various diseases. The radiomics research workflow is modality and disease independent which allow it to serve as a general platform for medical image post-processing experimentation.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Darryl H. Hwang, Darryl H. Hwang, Bino A. Varghese, Bino A. Varghese, Michael Chang, Michael Chang, Christopher Deng, Christopher Deng, Chidubem Ugweze, Chidubem Ugweze, Steven Y. Cen, Steven Y. Cen, Vinay A. Duddalwar, Vinay A. Duddalwar, } "Radiomics-based quantitative biomarker discovery: development of a robust image processing infrastructure ", Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 1016017 (26 January 2017); doi: 10.1117/12.2256829; https://doi.org/10.1117/12.2256829
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