A lightweight rapid application development framework for biomedical image analysis

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.

Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images

“…This FSM was created using the training surfaces from Dataset B, as per Xia et al's work (13). A total of three eigenmodes accounting for 92% of shape variations were visually inspected using the simple medical imaging library interface (SMILI) (19) and used in an effort to exclude any eigenmodes associated with cam morphology. This visual inspection consisted of a review of all shape variations within the first three eigenmodes within the anterior and anterosuperior femoral head-neck region.…”

“…A signed distance map was calculated between the reconstructed 'healthy' proximal femur and the cam-affected femur surface, using the Visualization Toolkit (VTK) (20) (Figure 1E). The distance map scalars were overlayed onto the femoral surface using SMILI (19).…”

Automated 3D Analysis of Clinical Magnetic Resonance Images Demonstrates Significant Reductions in Cam Morphology Following Arthroscopic Intervention in Contrast to Physiotherapy

“…Figure 4 shows 3D models of the proximal femur generated in SMILI 15 to visualize the baseline and 12-month cam profiles in individual PT male and female patients classified as having a baseline mild, moderate, or major cam volume, as calculated from the CamMorph pipeline. Topologically, there is very little (or no) observable difference in the cam location and profile in the femoral head-neck ROI between the baseline and 12-month comparisons for each of the individual male and female patients within each of the cam volume categories.…”

People Under Surveillance Tracker Prototype Development with Bluetooth Low Energy to Suppress the Covid-19 Spread

“…We utilize Fast Application Advancement (RAD) as an ODP Tracker model improvement strategy. Inquire about utilizing this strategy is [19]. The RAD strategy was chosen since this strategy was adjusted from the waterfall demonstration.…”