Baxter Wiberg (needtea7)

5ml. The parametric study showed the best results for the selected angle and geometry. The cadaver studies demonstrated the biomechanical efficacy of the low-volume tubular femoroplasty. The numerical calculations confirmed the optimal choice of positioning as well as the inner and outer diameter of the tube in this setting. The standardized minimally invasive technique with the instruments developed for it could be used in further comparative studies to confirm the measured biomechanical results. The cadaver studies demonstrated the biomechanical efficacy of the low-volume tubular femoroplasty. The numerical calculations confirmed the optimal choice of positioning as well as the inner and outer diameter of the tube in this setting. The standardized minimally invasive technique with the instruments developed for it could be used in further comparative studies to confirm the measured biomechanical results.Statistical shape analysis is a powerful tool to assess organ morphologies and find shape changes associated to a particular disease. However, imbalance in confounding factors, such as demographics might invalidate the analysis if not taken into consideration. Despite the methodological advances in the field, providing new methods that are able to capture complex and regional shape differences, the relationship between non-imaging information and shape variability has been overlooked. We present a linear statistical shape analysis framework that finds shape differences unassociated to a controlled set of confounding variables. It includes two confounding correction methods confounding deflation and adjustment. We applied our framework to a cardiac magnetic resonance imaging dataset, consisting of the cardiac ventricles of 89 triathletes and 77 controls, to identify cardiac remodelling due to the practice of endurance exercise. To test robustness to confounders, subsets of this dataset were generated by randomly removing controls with low body mass index, thus introducing imbalance. The analysis of the whole dataset indicates an increase of ventricular volumes and myocardial mass in athletes, which is consistent with the clinical literature. However, when confounders are not taken into consideration no increase of myocardial mass is found. Using the downsampled datasets, we find that confounder adjustment methods are needed to find the real remodelling patterns in imbalanced datasets.Ischemic stroke lesion and white matter hyperintensity (WMH) lesion appear as regions of abnormally signal intensity on magnetic resonance image (MRI) sequences. Ischemic stroke is a frequent cause of death and disability, while WMH is a risk factor for stroke. Accurate segmentation and quantification of ischemic stroke and WMH lesions are important for diagnosis and prognosis. However, radiologists have a difficult time distinguishing these two types of similar lesions. A novel deep residual attention convolutional neural network (DRANet) is proposed to accurately and simultaneously segment and quantify ischemic stroke and WMH lesions in the MRI images. Imiquimod DRANet inherits the advantages of the U-net design and applies a novel attention module that extracts high-quality features from the input images. Moreover, the Dice loss function is used to train DRANet to address data imbalance in the training data set. DRANet is trained and evaluated on 742 2D MRI images which are produced from the sub-acute ischemic stroke lesion segmentation (SISS) challenge. Empirical tests demonstrate that DRANet outperforms several other state-of-the-art segmentation methods. It accurately segments and quantifies both ischemic stroke lesion and WMH. Ablation experiments reveal that attention modules improve the predictive performance of DRANet.Ischemic stroke lesion segmentation from Computed Tomography Perfusion (CTP) images is important for accurate diagnosis of stroke in acute care units. However, it is challenged by low image contrast and