dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM

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.

Standardized representation of the LIDC annotations using DICOM

“…As an example, the same mechanisms for data encoding could be used for augmentation of the images and nodule annotations with the radiomics features derived from the nodule regions. This work utilizes tools developed earlier for interpreting XML annotations of LIDC 12 and for generating the standardized representations for image analysis results 14 .…”

An annotated test-retest collection of prostate multiparametric MRI

“…Series correspond to each of the different types of MR acquisitions of image data, and annotations of those acquisitions with the ROIs and measurements. Image series were generated by the imaging equipment, while the derived object series were created by the conversion tools provided in the dcmqi library 23 ( https://github.com/QIICR/dcmqi ).…”

“…First, we randomly selected a subset of encoded segmentations, and visually confirmed the consistency of the segmented ROIs as loaded from the DICOM representation and the non-DICOM format used for the data capture originally. For this purpose we used the 3D Slicer QuantitativeReporting module 22 , 23 (see Fig. 2 for an example visualization), which allows loading and display of image segmentations from DICOM SEG objects overlayed on the annotated image, and displays the associated segmentation-based measurements loaded from the corresponding DICOM SR objects.…”

“…The dataset can be explored using a number of publicly available tools. Individual image series, segmented regions and the measurements corresponding to those regions can be visualized using 3D Slicer software 22 ( http://slicer.org ) with the QuantitativeReporting extension installed 23 . Fig.…”

Standardized representation of the LIDC annotations using DICOM

“…As an example, the same mechanisms for data encoding could be used for augmentation of the images and nodule annotations with the radiomics features derived from the nodule regions. This work utilizes tools developed earlier for interpreting XML annotations of LIDC 12 and for generating the standardized representations for image analysis results 14 . The dataset produced as a result of this work is harmonized with other standardized collections already in TCIA 15 .…”