| Literature DB >> 29092948 |
Christian Herz1,2, Jean-Christophe Fillion-Robin3, Michael Onken4, Jörg Riesmeier5, Andras Lasso6, Csaba Pinter6, Gabor Fichtinger6, Steve Pieper7, David Clunie8, Ron Kikinis1,2,9,10, Andriy Fedorov11,2.
Abstract
Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided in the GitHub repository at https://github.com/qiicr/dcmqi Cancer Res; 77(21); e87-90. ©2017 AACR. ©2017 American Association for Cancer Research.Entities:
Mesh:
Year: 2017 PMID: 29092948 PMCID: PMC5675033 DOI: 10.1158/0008-5472.CAN-17-0336
Source DB: PubMed Journal: Cancer Res ISSN: 0008-5472 Impact factor: 12.701