Abstract. Recently, magnetic resonance imaging has revealed to be important for the evaluation of placenta's health during pregnancy. Quantitative assessment of the placenta requires a segmentation, which proves to be challenging because of the high variability of its position, orientation, shape and appearance. Moreover, image acquisition is corrupted by motion artifacts from both fetal and maternal movements. In this paper we propose a fully automatic segmentation framework of the placenta from structural T2-weighted scans of the whole uterus, as well as an extension in order to provide an intuitive pre-natal view into this vital organ. We adopt a 3D multi-scale convolutional neural network to automatically identify placental candidate pixels. The resulting classification is subsequently refined by a 3D dense conditional random field, so that a high resolution placental volume can be reconstructed from multiple overlapping stacks of slices. Our segmentation framework has been tested on 66 subjects at gestational ages 20-38 weeks achieving a Dice score of 71.95 ± 19.79% for healthy fetuses with a fixed scan sequence and 66.89 ± 15.35% for a cohort mixed with cases of intrauterine fetal growth restriction using varying scan parameters.

In this work, we describe the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. A key aim of CRIMSON is to create a software environment that makes powerful computational haemodynamics tools accessible to a wide audience, including clinicians and students, both within our research laboratories and throughout the community. The overall philosophy is to leverage best-in-class open source standards for medical image processing, parallel flow computation, geometric solid modelling, data assimilation, and mesh generation. It is actively used by researchers in Europe, North and South America, Asia, and Australia. It has been applied to numerous clinical problems; we illustrate applications of CRIMSON to real-world problems using examples ranging from pre-operative surgical planning to medical device design optimization.

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In modern clinical practice, planning access paths to volumetric target structures remains one of the most important and most complex tasks, and a physician's insufficient experience in this can lead to severe complications or even the death of the patient. In this paper, we present a method for safety evaluation and the visualization of access paths to assist physicians during preoperative planning. As a metaphor for our method, we employ a well-known, and thus intuitively perceivable, natural phenomenon that is usually called crepuscular rays. Using this metaphor, we propose several ways to compute the safety of paths from the region of interest to all tumor voxels and show how this information can be visualized in real-time using a multi-volume rendering system. Furthermore, we show how to estimate the extent of connected safe areas to improve common medical 2D multi-planar reconstruction (MPR) views. We evaluate our method by means of expert interviews, an online survey, and a retrospective evaluation of 19 real abdominal radio-frequency ablation (RFA) interventions, with expert decisions serving as a gold standard. The evaluation results show clear evidence that our method can be successfully applied in clinical practice without introducing substantial overhead work for the acting personnel. Finally, we show that our method is not limited to medical applications and that it can also be useful in other fields.

Fig. 1. Visualization of hurricane Isabel simulation data: amount of cloud water (blue-red) and upwind strength (yellow-green). At large distances our method converges to normal direct volume rendering to avoid aliasing (a). However, at smaller distances the user is able to interpret the exact data values for both variables simultaneously (b, c). For example, it is possible to see the high amount of cloud water (red) in the middle of high-velocity upstream (cyan). This is impossible with normal direct volume rendering (d).Abstract-Analysis of multivariate data is of great importance in many scientific disciplines. However, visualization of 3D spatiallyfixed multivariate volumetric data is a very challenging task. In this paper we present a method that allows simultaneous real-time visualization of multivariate data. We redistribute the opacity within a voxel to improve the readability of the color defined by a regular transfer function, and to maintain the see-through capabilities of volume rendering. We use predictable procedural noiserandom-phase Gabor noise -to generate a high-frequency redistribution pattern and construct an opacity mapping function, which allows to partition the available space among the displayed data attributes. This mapping function is appropriately filtered to avoid aliasing, while maintaining transparent regions. We show the usefulness of our approach on various data sets and with different example applications. Furthermore, we evaluate our method by comparing it to other visualization techniques in a controlled user study. Overall, the results of our study indicate that users are much more accurate in determining exact data values with our novel 3D volume visualization method. Significantly lower error rates for reading data values and high subjective ranking of our method imply that it has a high chance of being adopted for the purpose of visualization of multivariate 3D data.