Bentzen Aaen (blowgoal7)

, et al. Sexual Function Outcomes After Surgical Treatment of Penile Fracture. Sex Med 2021;9100353. Over the past decade, convolutional neural networks (CNNs) have revolutionized the field of medical image segmentation. Prompted by the developments in computational resources and the availability of large datasets, a wide variety of different two-dimensional (2D) and three-dimensional (3D) CNN training strategies have been proposed. However, a systematic comparison of the impact of these strategies on the image segmentation performance is still lacking. Therefore, this study aimed to compare eight different CNN training strategies, namely 2D (axial, sagittal and coronal slices), 2.5D (3 and 5 adjacent slices), majority voting, randomly oriented 2D cross-sections and 3D patches. These eight strategies were used to train a U-Net and an MS-D network for the segmentation of simulated cone-beam computed tomography (CBCT) images comprising randomly-placed non-overlapping cylinders and experimental CBCT images of anthropomorphic phantom heads. The resulting segmentation performances were quantitatively compared by calculating Dice similarity coefficients. In addition, all segmented and gold standard experimental CBCT images were converted into virtual 3D models and compared using orientation-based surface comparisons. The CNN training strategy that generally resulted in the best performances on both simulated and experimental CBCT images was majority voting. When employing 2D training strategies, the segmentation performance can be optimized by training on image slices that are perpendicular to the predominant orientation of the anatomical structure of interest. Such spatial features should be taken into account when choosing or developing novel CNN training strategies for medical image segmentation. The results of this study will help clinicians and engineers to choose the most-suited CNN training strategy for CBCT image segmentation. The results of this study will help clinicians and engineers to choose the most-suited CNN training strategy for CBCT image segmentation. Clinical decision support systems (CDSSs) are developed to support healthcare practitioners with decision-making about therapy and diagnosis' confirmation, among others. Although there are many advantages of using CDSSs, there are still many challenges in their adoption. Therefore, it is essential to ensure the quality of the system, so that it can be used confidently and securely. This study aims to propose a set of (sub)characteristics which should be considered in evaluating the quality-in-use of CDSSs, based on the ISO/IEC 25010 standard and on existing literature. We reviewed the existing literature on CDSS assessment and presented a list of quality characteristics evaluated. Ten quality characteristics and 56 sub-characteristics were identified and selected from the literature, in which usability was evaluated the most. An example of a scenario has been presented to illustrate our assessment approach of satisfaction and efficiency as important quality-in-use characteristics to be applied in the evaluation of a CDSS. The proposed approach will contribute in bridging the gap between the quality of CDSSs and their adoption. The proposed approach will contribute in bridging the gap between the quality of CDSSs and their adoption.The intestine is a vital organ mediating absorption of nutrients and water. Following tissue damage, the intestine mounts a remarkable regenerative response by reprogramming cellular identity to facilitate reinstatement of homeostasis. selleckchem Here we review recent advances within intestinal regenerative biology and the emerging concept of fetal-like reprogramming, in which the adult intestinal epithelium transiently enters a repair-associated state reminiscent of ontologically pre-existing stages. We focus on molecular mechanis