Multi-Atlas Segmentation of the Facial Nerve

Medical image segmentation is an important step to identify the shape and position of patient anatomy prior to surgical simulation, surgical rehearsal, and surgical planning. It is crucial that the facial nerve (FN) is segmented accurately as damage to this nerve can severely impact facial expression, speech, and taste. Manual segmentation provides accurate results but is time-consuming and labor-intensive; semi-automatic methods of segmentation are more feasible in a clinical setting and can provide accurate results with minimal user involvement. The objective of this work was to create a novel, open-source, multi-atlas based segmentation algorithm of the entire FN requiring minimal user intervention. Twenty-eight temporal bones were segmented producing an average Dice metric of 0.76 and an average Hausdorff distance of 0.17 mm which is similar to previously published algorithms. These results indicate that this segmentation approach can accurately segment the FN and greatly reduce time spent with manual segmentation

The Prevalence of Sexually Transmitted Infections in Papua New Guinea: A Systematic Review and Meta-Analysis

Patellofemoral osteoarthritis (PF OA) is more prevalent than previously thought and contributes to patient's suffering from knee OA. Synthesis of prevalence data can provide estimates of the burden of PF OA