Automatic Segmentation and Alignment of Uterine Shapes from 3D Ultrasound Data.

Background: The uterus is the most important organ in the female reproductive system. Its shape plays a critical role in fertility and pregnancy outcomes. Advances in medical imaging, such as 3D ultrasound, have significantly improved the exploration of the female genital tract, thereby enhancing gynecological healthcare.

Dr. KID: Direct Remeshing and K-Set Isometric Decomposition for Scalable Physicalization of Organic Shapes.

Dr. KID is an algorithm that uses isometric decomposition for the physicalization of potato-shaped organic models in a puzzle fashion. The algorithm begins with creating a simple, regular triangular surface mesh of organic shapes, followed by iterative K-means clustering and remeshing.

GPU Accelerated 3D Tomographic Reconstruction and Visualization From Noisy Electron Microscopy Tilt-Series.

We present a novel framework for 3D tomographic reconstruction and visualization of tomograms from noisy electron microscopy tilt-series. Our technique takes as an input aligned tilt-series from cryogenic electron microscopy and creates denoised 3D tomograms using a proximal jointly-optimized approach that iteratively performs reconstruction and denoising, relieving the users of the need to select appropriate denoising algorithms in the pre-reconstruction or post-reconstruction steps. The whole process is accelerated by exploiting parallelism on modern GPUs, and the results can be visualized immediately after the reconstruction using volume rendering tools incorporated in the framework.

Real-Time Interpolated Rendering of Terrain Point Cloud Data.

Most real-time terrain point cloud rendering techniques do not address the empty space between the points but rather try to minimize it by changing the way the points are rendered by either rendering them bigger or with more appropriate shapes such as paraboloids. In this work, we propose an alternative approach to point cloud rendering, which addresses the empty space between the points and tries to fill it with appropriate values to achieve the best possible output. The proposed approach runs in real time and outperforms several existing point cloud rendering techniques in terms of speed and render quality.

Finding Nano-Ötzi: Cryo-Electron Tomography Visualization Guided by Learned Segmentation.

Cryo-electron tomography (cryo-ET) is a new 3D imaging technique with unprecedented potential for resolving submicron structural details. Existing volume visualization methods, however, are not able to reveal details of interest due to low signal-to-noise ratio. In order to design more powerful transfer functions, we propose leveraging soft segmentation as an explicit component of visualization for noisy volumes.

Slovenian Validation of the Children's Perceived Use of Self-Regulated Learning Inventory.

The importance of self-regulated learning (SRL) has increased during the COVID-19 pandemic and measures for assessing students' self-regulation skills and knowledge are greatly needed. We present the results of the first thorough adaptation of the Children's Perceived use of Self-Regulated Learning Inventory (CP-SRLI). The inventory, consisting of 15 scales measuring nine components of SRL, was administered to a sample of 541 Slovenian ninth graders.

Nanotilus: Generator of Immersive Guided-Tours in Crowded 3D Environments.

Immersive virtual reality environments are gaining popularity for studying and exploring crowded three-dimensional structures. When reaching very high structural densities, the natural depiction of the scene produces impenetrable clutter and requires visibility and occlusion management strategies for exploration and orientation. Strategies developed to address the crowdedness in desktop applications, however, inhibit the feeling of immersion.

Web-Based 3D Visualisation of Biological and Medical Data.

In this chapter we present an overview of web-based frameworks for visualisation of medical and biological data, with emphasis on visualisation of volumetric data such as radiological data (e.g. magnetic resonance imaging, computed tomography or positron emission tomography) and microscopy data (e.

Automatic segmentation of mitochondria and endolysosomes in volumetric electron microscopy data.

Automatic segmentation of intracellular compartments is a powerful technique, which provides quantitative data about presence, spatial distribution, structure and consequently the function of cells. With the recent development of high throughput volumetric data acquisition techniques in electron microscopy (EM), manual segmentation is becoming a major bottleneck of the process. To aid the cell research, we propose a technique for automatic segmentation of mitochondria and endolysosomes obtained from urinary bladder urothelial cells by the dual beam EM technique.

Aerial LiDAR Data Augmentation for Direct Point-Cloud Visualisation.

Direct point-cloud visualisation is a common approach for visualising large datasets of aerial terrain LiDAR scans. However, because of the limitations of the acquisition technique, such visualisations often lack the desired visual appeal and quality, mostly because certain types of objects are incomplete or entirely missing (e.g.

V-RBNN Based Small Drone Detection in Augmented Datasets for 3D LADAR System.

A common countermeasure to detect threatening drones is the electro-optical infrared (EO/IR) system. However, its performance is drastically reduced in conditions of complex background, saturation and light reflection. 3D laser sensor LiDAR is used to overcome the problems of 2D sensors like EO/IR, but it is not enough to detect small drones at a very long distance because of low laser energy and resolution.