RESULTS AND CONCLUSIONS The experimental results show that a new pair of images can be accurately registered using the trained registration model. In comparison with the conventional state-of-the-art method, SyN, the invertibility of the deformation field, accuracy and speed are all improved. Compared with the deep learning based method, VoxelMorph, the proposed method improves the invertibility of the deformation field. The Wasserstein distance is a powerful metric based on the theory of optimal mass transport. It gives a natural measure of the distance between two distributions with a wide range of applications. In contrast to a number of the common divergences on distributions such as Kullback-Leibler or Jensen-Shannon, it is (weakly) continuous, and thus ideal for analyzing corrupted and noisy data. Until recently, however, no kernel methods for dealing with nonlinear data have been proposed via the Wasserstein distance. In this work, we develop a novel method to compute the L2-Wasserstein distance in reproducing kernel Hilbert spaces (RKHS) called kernel L2-Wasserstein distance, which is implemented using the kernel trick. The latter is a general method in machine learning employed to handle data in a nonlinear manner. We evaluate the proposed approach in identifying computed tomography (CT) slices with dental artifacts in head and neck cancer, performing unsupervised hierarchical clustering on the resulting Wasserstein distance matrix that is computed on imaging texture features extracted from each CT slice.  https://www.selleckchem.com/products/mrtx0902.html  We further compare the performance of kernel Wasserstein distance with alternatives including kernel Kullback-Leibler divergence we previously developed. Our experiments show that the kernel approach outperforms classical non-kernel approaches in identifying CT slices with artifacts. Exposure of lung airways to detrimental suspended aerosols in the environment increases the vulnerability of the respiratory and cardiovascular systems. In addition, recent developments in therapeutic inhalation devices magnify the importance of particle transport. In this manuscript, particle transport and deposition patterns in the upper tracheobronchial (TB) tree were studied where the inertial forces are considerable for microparticles. Wall shear stress divergence (WSSdiv) is proposed as a wall-based parameter that can predict particle deposition patterns. WSSdiv is proportional to near-wall normal velocity and can quantify the strength of flow towards and away from the wall. Computational fluid dynamics (CFD) simulations were performed to quantify airflow velocity and WSS vectors for steady inhalation in one case-control and unsteady inhalation in six subject-specific airway trees. Turbulent flow simulation was performed for the steady case using large eddy simulation to study the effect of turbulence. investigated. This study shows that WSSdiv could be used to predict the qualitative surface deposition and relative regional dosimetry without the need to solve a particle transport problem. Delineation of tumours in Positron Emission Tomography (PET) plays a crucial role in accurate diagnosis and radiotherapy treatment planning. In this context, it is of outmost importance to devise efficient and operator-independent segmentation algorithms capable of reconstructing the tumour three-dimensional (3D) shape. In previous work, we proposed a system for 3D tumour delineation on PET data (expressed in terms of Standardized Uptake Value - SUV), based on a two-step approach. Step 1 identified the slice enclosing the maximum SUV and generated a rough contour surrounding it. Such contour was then used to initialize step 2, where the 3D shape of the tumour was obtained by separately segmenting 2D PET slices, leveraging the slice-by-slice marching approach. Additionally, we combined active contours and machine learning components to improve performance. Despite its success, the slice marching approach poses unnecessary limitations that are naturally removed by performing the segmentation directly in 3D. In this paper, we migrate our system into 3D. In particular, the segmentation in step 2 is now performed by evolving an active surface directly in the 3D space. The key points of such an advancement are that it performs the shape reconstruction on the whole stack of slices simultaneously, naturally leveraging cross-slice information that could not be exploited before. Additionally, it does not require any specific stopping condition, as the active surface naturally reaches a stable topology once convergence is achieved. Performance of this fully 3D approach is evaluated on the same dataset discussed in our previous work, which comprises fifty PET scans of lung, head and neck, and brain tumours. The results have confirmed that a benefit is indeed achieved in practice for all investigated anatomical districts, both quantitatively, through a set of commonly used quality indicators (dice similarity coefficient >87.66%, Hausdorff distance 3 in 54% of the tumours). PURPOSE To evaluate the impact of different supervision regimens on the training of artificial intelligence (AI) in the classification of chest radiographs as normal or abnormal in a moderately sized cohort of individuals more likely to be outpatients. MATERIALS AND METHODS In a retrospective study, 7000 consecutive two-view chest radiographs obtained from 2012 to 2015 were labeled as normal or abnormal based on clinical reports. A convolutional neural network (CNN) was trained on this dataset and then evaluated with an unseen subset of 500 radiographs. Five different training approaches were tested (1) weak supervision and four hybrid approaches combining weak supervision and extra supervision with annotation in (2) an unbalanced set of normal and abnormal cases, (3) a set of only abnormal cases, (4) a set of only normal cases, and (5) a balanced set of normal and abnormal cases. Standard binary classification metrics were assessed. RESULTS The weakly supervised model achieved an accuracy of 82%, but yielded 75 false negative cases, at a sensitivity of 70.0% and a negative predictive value (NPV) of 75.5%. Extra supervision increased NPV at the expense of the false positive rate and overall accuracy. Extra supervision with training using a balance of abnormal and normal radiographs resulted in the greatest increase in NPV (87.2%), improved sensitivity (92.8%), and reduced the number of false negatives by more than fourfold (18 compared to 75 cases). CONCLUSION Extra supervision using a balance of annotated normal and abnormal cases applied to a weakly supervised model can minimize the number of false negative cases when classifying two-view chest radiographs. Further refinement of such hybrid training approaches for AI is warranted to refine models for practical clinical applications.