Elsevier

Magnetic Resonance Imaging

Volume 30, Issue 9, November 2012, Pages 1323-1341
Magnetic Resonance Imaging

Original contribution
3D Slicer as an image computing platform for the Quantitative Imaging Network

https://doi.org/10.1016/j.mri.2012.05.001Get rights and content

Abstract

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside.

3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions.

In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.

Introduction

Cancer is the leading cause of death in the developed world and the second leading cause of death in the developing countries [1]. With the incidence of cancer rapidly increasing, there is an immediate need for better understanding of this disease and for the development of the targeted, personalized treatment approaches. Successful translation of such treatments from the lab to the clinic is contingent on the availability of biomarkers — objective and testable characteristics indicative of normal or pathologic processes that ideally should allow for quantitative measurement of the response to therapy [2], [3]. In this regard, in vivo imaging biomarkers are particularly promising, as they can be highly specific and minimally invasive, providing both anatomical and functional understanding of the response patterns. However, the potential of quantitative imaging remains largely underutilized. The Response Evaluation Criteria in Solid Tumors — the only imaging-based biomarker accepted by the US FDA as a surrogate end point for clinical outcome in therapy — rely primarily on the anatomical imaging of the lesion measured by its largest diameter [4], [5]. Continuous advances in multimodality three-dimensional (3D) imaging technology and analysis, along with improvements in computer science and bioinformatics, create an opportunity for a paradigm shift in quantification of treatment response. To advance the role of imaging as a biomarker of treatment, the National Cancer Institute launched the Quantitative Imaging Network (QIN) initiative [6]. The goal of QIN is to form a community of interdisciplinary teams engaged in the development of imaging-based biomarkers and their optimization in the context of clinical trials. Research software platforms are essential in prototyping, development and evaluation of novel algorithmic methods as a mechanism for discovering image-based surrogate end points. Such platforms should also support integration of the algorithmic advances into the clinical trial work flows. In this paper, we discuss the capabilities and the utility of 3D Slicer (Slicer) as an enabling research platform for quantitative image computing research.

3D Slicer is a free open-source extensible software application for medical image computing and visualization. Slicer emerged as a culmination of several independent projects that focused separately on image visualization, surgical navigation and graphical user interface (GUI). David Gering presented the initial prototype of the Slicer software in his MIT Master's thesis in 1999 [7] based on the earlier experience of the research groups at MIT and Surgical Planning Lab (SPL) [8]. Subsequently, Steve Pieper assumed the role of the Chief Architect, commencing the work of transforming 3D Slicer into an industrial-strength package. Since 1999, Slicer has been under continuous development at the SPL under the leadership of Ron Kikinis. Today it is developed mostly by professional engineers in close collaboration with algorithm developers and application domain scientists, with the participation of Isomics Inc., Kitware Inc. and GE Global Research and with significant contributions from the growing Slicer community. Initially envisioned as a neurosurgical guidance, visualization and analysis system [7], [9], over the last decade, Slicer has evolved into an integrated platform that has been applied in a variety of clinical and preclinical research applications, as well as for the analysis of nonmedical images [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21]. Improvement and maintenance of the software have been possible primarily through the support from the National Institutes of Health (NIH). At the same time, its development has grown into a community effort, as numerous groups and individual users not funded directly to develop 3D Slicer are continuously improving it by reporting software problems and contributing solutions, suggesting new features and developing new tools. As described more fully below, Slicer integrates a number of powerful open-source projects into an end-user application suitable for clinical researchers.

The breadth of functionality, extensibility, portability across platforms and nonrestrictive software license are some of the main features that differentiate Slicer from the commercial and open-source software tools and workstations that aim to cover similar aspects of functionality. Numerous choices of radiology workstations and image analysis tools are available from the commercial vendors. Some of the popular tools used in the clinic as well as in research are AW Workstation (GE Healthcare), syngo.via (Siemens), PMOD (PMOD Technologies Ltd., Zurich, Switzerland), Definiens (Definiens Inc., Parsippany, NJ, USA), MimVista (MIM Software Inc., Cleveland, OH, USA). These packages provide users with a set of analysis tools (some of which may be specifically approved by the FDA for certain clinical tasks), compatibility with the clinical Picture Archiving and Communication System (PACS) systems and customer support. Such clinically oriented systems are not always affordable to the academic researchers. Commercial solutions are typically not extensible by the end user, are not oriented towards prototyping of the new tools and may require specialized hardware, limiting their applicability in projects that involve development of new image analysis methods. In the research domain, MATLAB (Mathworks, Natick, MA, USA) has traditionally been the “Swiss army knife” of scientific computing. Many researchers use MATLAB for initial prototyping and experimentation, while some end-user tools, such as SPM [22], are built on top of MATLAB. Being a generic prototyping tool, MATLAB is not designed for medical applications and thus lacks support for interface and display conventions common in clinical environments. As a result, deployment of the developed tools for the use by clinical researchers requires translation of the code into more generic languages to minimize dependencies and simplify integration.

As opposed to the commercial workstations, 3D Slicer is meant to provide a research platform that is freely available and does not require specialized equipment. Slicer's use is not constrained to a single processing task or research application. Its generality and extensibility separate Slicer from such task-oriented packages as ITK-Snap (image segmentation) [23], DtiStudio (diffusion tensor analysis) [24], FreeSurfer,1 FSL [25] and SPM [22] (neuroimaging applications). Several other tools, such as OsiriX [26], BioImage Suite,2 MIPAV [27] and ImageJ3 [28], are similar to Slicer in that they provide extensible development platforms for biomedical imaging applications (for a comprehensive comparison of these tools, we refer the reader to the earlier surveys [29], [30]). ImageJ is an extensible Java-based image processing platform that has been applied to a variety of applications, including radiological image processing [28], with the focus on two-dimensional (2D) analysis. MIPAV is a cross-platform Java-based package supported by NIH [27]. OsiriX is an open-source PACS workstation and DICOM viewer for Mac OS X that provides advanced capabilities such as image fusion, volume rendering and image annotation, and is extensible via a documented plug-in mechanism [26]. ClearCanvas is a Windows-based DICOM workstation adopted by the caBIG project.4 The TCGA version of ClearCanvas workstation supports AIM model annotation capabilities [31], [32] and is also extensible. A notable aspect of both OsiriX and ClearCanvas is that these systems are made available either in a free open-source version or as commercial, FDA-cleared products. A practical shortcoming is their dependency on specific operating systems (Mac OS X for OsiriX and MS Windows for ClearCanvas). Perhaps more importantly, some of the aforementioned packages that are similar in their intended purpose to 3D Slicer (including BioImage Suite, MIPAV, OsiriX and ClearCanvas) are distributed under restrictive open-source licenses that limit the ability of outside developers to redistribute parts of those systems, in particular, in commercial or other “closed-source” scenarios. This can be a practical constraint for QIN investigators collaborating with industry partners, as the solutions developed on top of these packages cannot be directly incorporated into commercial products. Another consideration is that critical functionality to work with modern imaging scenarios may only be available in the commercial version of the package. OsiriX and ClearCanvas, for example, do not support 64-bit processing architectures in their royalty-free versions, and this limits the maximum size of the image data the software can accept.

3D Slicer does not have any components specific to a particular operating system. Binary distributions are available for 32- or 64-bit versions of Windows, Mac OS X or Linux, and the software can be compiled on other systems, such as Oracle's Solaris. It is distributed under a BSD-style license agreement [33] allowing free distribution of derivative software for academic and commercial use. Hence, image analysis tools developed within 3D Slicer can be adopted directly by the industry collaborators. Since new technologies can only become part of routine clinical care through their incorporation into FDA-regulated medical products, Slicer's permissive software license furthers the overall goal of lowering the barriers for translation of the successful research solutions into medical products. On the other hand, Slicer is not an FDA-approved device, and its license makes no claims about the clinical applicability of the software. It is the sole responsibility of the user to comply with appropriate safety and ethics guidelines, and any products incorporating Slicer technology must be fully tested to comply with applicable laws and regulations. Under these considerations, Slicer has been applied in a variety of projects under appropriate research oversight. In this manuscript, we aim to introduce the capabilities of 3D Slicer as a software platform for clinical imaging research and outline its use in the context of biomarker development for cancer treatment by several QIN sites.

In the remainder of this paper, we first present an overview of 3D Slicer by discussing its architecture, main features and guiding development principles. Next, we focus on the capabilities of 3D Slicer viewed from the perspective of the clinical researcher. We follow with the overview of the 3D Slicer platform from the standpoint of a biomedical engineer and discuss how Slicer can facilitate development of new software tools for clinical research. To demonstrate how 3D Slicer is currently used by some of the existing teams of QIN, we discuss the clinical research projects investigated at Brigham and Women's Hospital (BWH) (PI Fiona Fennessy), University of Iowa (PI John Buatti) and Massachusetts General Hospital (MGH) (PI Bruce Rosen). We conclude with the summary of our findings, discussing some of the features and functionalities that would further improve applicability of 3D Slicer to biomarker development by the QIN investigators.

Section snippets

Overview of 3D Slicer

Computerized image analysis plays an increasing role in supporting clinical and research needs. Promising methodologies that may lead to new imaging biomarkers often involve custom image processing software. The role of software evolves over the different stages of the imaging biomarker life cycle. In the inception stage, promising methodological concepts are identified and translated into early prototypes. Such early prototypes are often cobbled together from parts of tools designed for other

Clinical research platform

From the perspective of a clinical researcher, Slicer is an advanced image visualization and analysis workstation, and it shares a lot of generic functionality with the commercially available packages. Unlike most of the commercially available workstations, 3D Slicer is not an FDA-cleared product, and its intended use is clinical research applications. In this sense, Slicer is very different from its commercial counterparts since it incorporates experimental tools that cannot be packaged within

Research development platform

One of the main goals of 3D Slicer is to provide biomedical engineers, developers and applied scientists with the essential components for quick prototyping and efficient development of biomedical image analysis tools. From the beginning, Slicer has been designed to be open and extensible. As a platform, it establishes the interfaces and design patterns for developing new functionality and integrating the existing components into new tools. New functionality is introduced into Slicer in the

Brigham and Women's Hospital

The objective of the BWH QIN site (PI Fiona Fennessy) is to study MR analysis tools and algorithms for detection of prostate cancer (PCa) and disease recurrence, and as a guide for targeted prostate therapy. There is increasing evidence [55], [56] that MRI can facilitate the detection and characterization of PCa through the use of a combination of multiparametric MRI (mpMRI) techniques [such as diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI], in addition to the

Discussion

We presented three use cases of 3D Slicer that demonstrate how this research platform is currently applied to study cancer in different organs using a variety of imaging modalities by the existing QIN sites. Although the specific research goals of these groups are different, they leverage generic capabilities of Slicer such as processing tools, multimodal display, image fusion and reconfigurable GUI layouts. Common to the use cases is the need to consistently organize and present a variety of

Acknowledgments

We would like to thank all current and past users and developers of 3D Slicer for their contribution to this software. The authors have been supported in part by the following NIH grants. BWH: U01CA151261, P41EB015898, P41RR13218, U54EB005149 and 1R01CA111288; University of Iowa: U01-CA140206; GE: P41RR13218 and U54EB005149; MGH: 1U01CA154601-01 and 4R00LM009889-03. We are grateful to the various agencies and programs that funded support and development of 3D Slicer over the years.

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