A lightweight rapid application development framework for biomedical image analysis. (October 2018) - Chandra, Shekhar S.; Dowling, Jason A.; Engstrom, Craig; Xia, Ying; Paproki, Anthony; Neubert, Aleš; Rivest-Hénault, David; Salvado, Olivier; Crozier, Stuart; Fripp, Jurgen
Highlights: The work presents a new biomedical image analysis and visualization framework for multiple platforms with an open-source license suitable for commercial use. This Simple Medical Imaging Library Interface (SMILI) is designed for creating easy-to-use applications for clinical end-users ideal for deploying biomedical image analysis algorithms. This submission is also part of an open-source release of the above library at SourceForge (https://smili-project.sourceforge.io/ ) and GitHub (https://github.com/shakes76/smili ). The library is compared to other state-of-the-art biomedical image analysis software packages and validated for clinical usability in the context of bone deformities of the hip joint. Abstract: Biomedical imaging analysis typically comprises a variety of complex tasks requiring sophisticated algorithms and visualising high dimensional data. The successful integration and deployment of the enabling software to clinical (research) partners, for rigorous evaluation and testing, is a crucial step to facilitate adoption of research innovations within medical settings. In this paper, we introduce the Simple Medical Imaging Library Interface (SMILI), an object oriented open-source framework with a compact suite of objects geared for rapid biomedical imaging (cross-platform) application development and deployment. SMILI supports the development of both command-line (shell and Python scripting) and graphical applications utilising the same set of processing algorithms. It provides a substantial subset of features when compared to more complex packages, yet it is small enough to ship with clinical applications with limited overhead and has a license suitable for commercial use. After describing where SMILI fits within the existing biomedical imaging software ecosystem, by comparing it to other state-of-the-art offerings, we demonstrate its capabilities in creating a clinical application for manual measurement of cam-type lesions of the femoral head-neck region for the investigation of femoro-acetabular impingement (FAI) from three dimensional (3D) magnetic resonance (MR) images of the hip. This application for the investigation of FAI proved to be convenient for radiological analyses and resulted in high intra (ICC=0.97) and inter-observer (ICC=0.95) reliabilities for measurement of α -angles of the femoral head-neck region. We believe that SMILI is particularly well suited for prototyping biomedical imaging applications requiring user interaction and/or visualisation of 3D mesh, scalar, vector or tensor data.