IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics

Abstract

Purpose: Radiomics, which is the high-throughput extraction and analysis of quantitative image features, has been shown to have considerable potential to quantify the tumor phenotype. However, at present, a lack of software infrastructure has impeded the development of radiomics and its applications. Therefore, the authors developed the imaging biomarker explorer (IBEX), an open infrastructure software platform that flexibly supports common radiomics workflow tasks such as multimodality image data import and review, development of feature extraction algorithms, model validation, and consistent data sharing among multiple institutions.

Methods: The IBEX software package was developed using the MATLAB and c/c++ programming languages. The software architecture deploys the modern model-view-controller, unit testing, and function handle programming concepts to isolate each quantitative imaging analysis task, to validate if their relevant data and algorithms are fit for use, and to plug in new modules. On one hand, IBEX is self-contained and ready to use: it has implemented common data importers, common image filters, and common feature extraction algorithms. On the other hand, IBEX provides an integrated development environment on top of MATLAB and c/c++, so users are not limited to its built-in functions. In the IBEX developer studio, users can plug in, debug, and test new algorithms, extending IBEX's functionality. IBEX also supports quality assurance for data and feature algorithms: image data, regions of interest, and feature algorithm-related data can be reviewed, validated, and/or modified. More importantly, two key elements in collaborative workflows, the consistency of data sharing and the reproducibility of calculation result, are embedded in the IBEX workflow: image data, feature algorithms, and model validation including newly developed ones from different users can be easily and consistently shared so that results can be more easily reproduced between institutions.

Results: Researchers with a variety of technical skill levels, including radiation oncologists, physicists, and computer scientists, have found the IBEX software to be intuitive, powerful, and easy to use. IBEX can be run at any computer with the windows operating system and 1GB RAM. The authors fully validated the implementation of all importers, preprocessing algorithms, and feature extraction algorithms. Windows version 1.0 beta of stand-alone IBEX and IBEX's source code can be downloaded.

Conclusions: The authors successfully implemented IBEX, an open infrastructure software platform that streamlines common radiomics workflow tasks. Its transparency, flexibility, and portability can greatly accelerate the pace of radiomics research and pave the way toward successful clinical translation.

FIG. 1.

The ibex architecture. MVC, unit testing, and function handle programming concepts are deployed to isolate each task, test algorithms, plug in new algorithms, share data, and reproduce data easily and consistently.

FIG. 2.

The ibex workflow. Regular users import data, prepare the data set and feature set, specify the model formula, and compute the feature value and/or model value. Advanced users can plug in new data format importers, preprocessing methods, feature algorithms, and test review methods using the ibex developer studio.

FIG. 3.

Example of a DICOM data importer. The importer sorts and organizes DICOM data based on the relationship among MRNs, instance UIDs, study UIDs, series UIDs, and frame UIDs, and then lists all the available patients that could be imported. The Details list box describes the detailed patient information for verification.

FIG. 4.

The ibex image data workspace. The main purpose of this workspace is to insert data set items by specifying image and ROI pairs. Image data can be viewed in axial, coronal, and sagittal orientations. ROIs can be overlaid on images and modified if necessary. Users can navigate to different image slices, zoom images in and out, quickly view the corresponding anatomy using the intersection tool, measure the distance, check the image intensity value, manually set window/level, select the preset window/level setting, and select the preset color map.

FIG. 5.

The ROI editor tools in ibex. Users can use the ROI editor to create new ROIs, copy existing ROIs, delete ROIs, nudge contours, delete contours, draw contours by clicking points, freely draw contours, and interpolate contours.

FIG. 6.

The feature algorithm workspace in ibex. The main purpose of this workspace is to prepare the feature set by specifying the image preprocessing algorithms, feature category, and feature algorithms.

FIG. 7.

A testing GUI in ibex. At each stage (import, preprocessing, and feature calculation), users have the option of reviewing the corresponding results and intermediate data.

FIG. 8.

Self-documented algorithm in ibex. The algorithm and feature name are self-explained. The description of the algorithm and its parameters can be easily accessed using the help button on the parameter modification GUI (circled in red).

FIG. 9.

The appropriate algorithm parameters for different modality images. (A) PET image. (B) CT-type parameters. (C) PET-type parameters. (D) Histogram from CT-type parameters that is meaningless and squeezed into one bin. (E) Histogram from PET-type parameters. The PET-type parameters zoom in on a CT-type histogram and can provide meaningful results for a PET image.