Integration of 3D Slicer and BioImage Suite

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DESCRIPTION (provided by applicant): While the increasing availability of open source software for medical image analysis has opened up new avenues for the integration of algorithms from different sources for complex tasks, integration at the source code level is a non-trivial process. For example, the various algorithms may be written in different programming languages and/or require a large assortment of underlying libraries. These issues make the source integration of such algorithms often an impossibly complex and error-prone task. An alternative strategy, which we adopt for the proposed work is the use of binary integration. In this scenario, the algorithms are already precompiled in binary form and are integrated together using a standard interface in a manner similar to the use of plugins in web browsers. This strategy has major potential advantages including code license and development framework neutrality, as all that needs to be standardized is the invocation of the various algorithms. In this work, we propose to generalize and standardize an existing binary interface developed by the 3D Slicer team and use this to make functionality from the Yale BioImage Suite software package and 3D Slicer seamlessly available to the "other" package. To this end, we propose two specific aims: (1) Extend and generalize the Slicer Execution Layer to incorporate additional file formats and objects to enable the integration and (2) Extend the Yale BioImage Suite software package to fully implement this layer. With the development of this framework, we hope to pave the way for further interoperability among other image analysis packages. This integration will eventually provide users with a large collection of tools that they need for data analysis and allow developers to focus on developing novel image analysis algorithms. We anticipate that, with the adoption of this type of interface, a portal such as NITRC could thus be eventually transformed into a repository for interoperable components that could easily be integrated to perform complex image analysis tasks without requiring all developers to implement their algorithms using the exact same underlying software engineering framework. Instead, developers would only need to standardize how their algorithms are invoked by external applications which is a much more tractable task. This type of interface could also enable, in the future, the use of such algorithms as internet services, thus potentially even eliminating the need for local software installation. PUBLIC HEALTH RELEVANCE: Relevance to Public Health The development of standardized integration interfaces for medical image analysis will enable researchers to easily and seamlessly combine image analysis algorithms from a variety of sources to accomplish complex anal- ysis of their data. This integration will, in particular, enable newer and more effective algorithms to be adopted by the medical imaging user community without them having to be officially reimplemented and integrated into larger software packages. Rather, such algorithms could be designed to be available as standard plugins, in the same way that plugins are used in web browsers, and integrated by the users themselves in their customized workflows.