Objective To evaluate the agreement, accuracy, and longitudinal reproducibility of quantitative cartilage morphometry from 2D U-Net-based automated segmentations for 3T coronal fast low angle shot (corFLASH) and sagittal double echo at steady-state (sagDESS) MRI. Methods 2D U-Nets were trained using manual, quality-controlled femorotibial cartilage segmentations available for 92 Osteoarthritis Initiative healthy reference cohort participants from both corFLASH and sagDESS (n = 50/21/21 training/validation/test-set). Cartilage morphometry was computed from automated and manual segmentations for knees from the test-set. Agreement and accuracy were evaluated from baseline visits (dice similarity coefficient: DSC, correlation analysis, systematic offset). The longitudinal reproducibility was assessed from year-1 and -2 follow-up visits (root-mean-squared coefficient of variation, RMSCV%). Results Automated segmentations showed high agreement (DSC 0.89–0.92) and high correlations (r ≥ 0.92) with manual ground truth for both corFLASH and sagDESS and only small systematic offsets (≤ 10.1%). The automated measurements showed a similar test–retest reproducibility over 1 year (RMSCV% 1.0–4.5%) as manual measurements (RMSCV% 0.5–2.5%). Discussion The 2D U-Net-based automated segmentation method yielded high agreement compared with manual segmentation and also demonstrated high accuracy and longitudinal test–retest reproducibility for morphometric analysis of articular cartilage derived from it, using both corFLASH and sagDESS.

Vertebroplasty and kyphoplasty are commonly used minimally invasive methods to treat vertebral compression fractures. Novice surgeons gather surgical skills in different ways, mainly by "learning by doing" or training on models, specimens or simulators. Currently, a new training modality, an augmented reality simulator for minimally invasive spine surgeries, is going to be developed. An important step in investigating this simulator is the accurate establishment of artificial tissues. Especially vertebrae and muscles, reproducing a comparable haptical feedback during tool insertion, are necessary. Two artificial tissues were developed to imitate natural muscle tissue. The axial insertion force was used as validation parameter. It appropriates the mechanical properties of artificial and natural muscles. Validation was performed on insertion measurement data from fifteen artificial muscle tissues compared to human muscles measurement data. Based on the resulting forces during needle insertion into human muscles, a suitable material composition for manufacturing artificial muscles was found.

No abstract

No abstract

Objective. To study the longitudinal performance of fully automated cartilage segmentation in knees with radiographic osteoarthritis (OA), we evaluated the sensitivity to change in progressor knees from the Foundation for the National Institutes of Health OA Biomarkers Consortium between the automated and previously reported manual expert segmentation, and we determined whether differences in progression rates between predefined cohorts can be detected by the fully automated approach.Methods. The OA Initiative Biomarker Consortium was a nested case-control study. Progressor knees had both medial tibiofemoral radiographic joint space width loss (≥0.7 mm) and a persistent increase in Western Ontario and McMaster Universities Osteoarthritis Index pain scores (≥9 on a 0-100 scale) after 2 years from baseline (n = 194), whereas non-progressor knees did not have either of both (n = 200). Deep-learning automated algorithms trained on radiographic OA knees or knees of a healthy reference cohort (HRC) were used to automatically segment medial femorotibial compartment (MFTC) and lateral femorotibial cartilage on baseline and 2-year follow-up magnetic resonance imaging. Findings were compared with previously published manual expert segmentation.Results. The mean AE SD MFTC cartilage loss in the progressor cohort was -181 AE 245 μm by manual segmentation (standardized response mean [SRM] -0.74), -144 AE 200 μm by the radiographic OA-based model (SRM -0.72), and -69 AE 231 μm by HRC-based model segmentation (SRM -0.30). Cohen's d for rates of progression between progressor versus the non-progressor cohort was -0.84 (P < 0.001) for manual, -0.68 (P < 0.001) for the automated radiographic OA model, and -0.14 (P = 0.18) for automated HRC model segmentation. Conclusion.A fully automated deep-learning segmentation approach not only displays similar sensitivity to change of longitudinal cartilage thickness loss in knee OA as did manual expert segmentation but also effectively differentiates longitudinal rates of loss of cartilage thickness between cohorts with different progression profiles.ClinicalTrials.gov identifier: NCT00080171. Scientific and financial support for the Foundation for the NIH (FNIH) Osteoarthritis (OA) Biomarkers Consortium and for this study has been made possible through grants as well as direct and in-kind contributions from Abb-Vie, Amgen, Inc.,

No abstract

Surgical simulators provide a safe environment where novice surgeons can acquire their surgical skills. Although the number of patients with diseases of the musculoskeletal system is growing, the development of orthopedic simulators is still in it's infancy. The aim of this work was to identify simulation-based assessment parameters for a novel simulator in minimally invasive spine surgery. Apart from parameters targeting the duration and the surgeon's economy of motion during percutaneous bone access, parameters characterizing the movement smoothness were also examined with respect to their suitability. The results indicated, that the overall duration, the number of instrument movements, the number of velocity peaks and the Movement Arrest Period Ratio are the most promising predictors of expertise. Targeting performance improvement, the overall duration (p = 0.001), the number of instrument movements (p = 0.003) and the traveled instrument path length (p = 0.009) detected significant differences between subsequent trials. Using these parameters, a study can be designed targeting the validity and reliability of the simulation-based assessment.

Currently the surgical training of kyphoplasty and vertebroplasty is performed on patients or specimens. To improve patient safety, a project was initiated to develop an Augmented Reality simulator for the surgical training of these interventions. Artificial vertebral segments should be integrated to provide realistic haptic feedback. To reach this, resulting forces during needle insertions (trans- and extrapedicular) into formalin-fixed vertebral specimens were measured. The same insertion procedure was also performed on six customized polyurethane blocks with varying mechanical parameters. Based on the results of these measurements, a specific foam phantom was generated and the insertion force measured. Additionally a parametric model for the needle insertion into bone was designed calculating three characteristic parameters for all insertion measurements. The resulting insertion force for the foam phantom was comparable to the specimen measurements and the parametric model provided comprehensible characteristic parameters. Based on the resulting force during needle insertion into human vertebrae, a possible foam recipe for manufacturing artificial segments was found. Furthermore, the parametric model provides characteristic parameters for the assessment of phantoms as well as the development of its production process.