Multilabel segmentation and analysis of skeletal muscle and adipose tissue in routine abdominal CT scans.

Purpose: This paper presents a deep learning-based multi-label segmentation network that extracts a total of three separate adipose tissues and five different muscle tissues in CT slices of the third lumbar vertebra and additionally improves the segmentation of the intermuscular fat.

Method: Based on a self-created data set of 130 patients, an extended Unet structure was trained and evaluated with the help of Dice score, IoU and Pixel Accuracy. In addition, the interobserver variability for the decision between ground truth and post-processed segmentation was calculated to illustrate the relevance in everyday clinical practice.

A virtual patient authoring tool for transcatheter aortic valve replacement.

Purpose: Computer-based medical training scenarios, derived from patient's records, often lack variability, modifiability, and availability. Furthermore, generating image datasets and creating scenarios is resource-intensive. Therefore, patient authoring tools for rapid dataset-independent creation of virtual patients (VPs) is a pressing need.

[Virtual reality in liver surgery-Planning, advanced training, testing].

Anatomical structures of the liver could be reconstructed three dimensionally from preoperative cross-sectional imaging for over 20 years. This three-dimensional (3D) representation not only optimizes the preoperative planning options but also the communication of specific operation-relevant structures can be improved using 3D models. In addition to a plastic and interactive visualization of 3D organ models, the disruptive technology of virtual reality (VR) can also provide a possibility for structured training and further education regarding surgical anatomy of the liver and operation planning.

Assessment of intracranial aneurysm neck deformation after contour deployment.

Purpose: The contour neurovascular system (CNS) is a novel device to treat intracranial wide-necked bifurcation aneurysms, with few studies assessing its long-term effects. Particularly its impact on aneurysm morphology has not been explored yet. We present a preliminary study to explore this impact for the first time, focusing on the neck curve and ostium of the aneurysm.

Application of immersive virtual reality for assessing chronic neglect in individuals with stroke: the immersive virtual road-crossing task.

Background: Neglect can be a long-term consequence of chronic stroke that can impede an individual's ability to perform daily activities, but chronic and discrete forms can be difficult to detect. We developed and evaluated the "immersive virtual road-crossing task" (iVRoad) to identify and quantify discrete neglect symptoms in chronic stroke patients.

Method: The iVRoad task requires crossing virtual intersections and placing a letter in a mailbox placed either on the left or right.

Surgical phase and instrument recognition: how to identify appropriate dataset splits.

Purpose: Machine learning approaches can only be reliably evaluated if training, validation, and test data splits are representative and not affected by the absence of classes. Surgical workflow and instrument recognition are two tasks that are complicated in this manner, because of heavy data imbalances resulting from different length of phases and their potential erratic occurrences. Furthermore, sub-properties like instrument (co-)occurrence are usually not particularly considered when defining the split.

Comparison of aortic blood flow rotational direction in healthy volunteers and patients with bicuspid aortic valves using volumetric velocity-sensitive cardiovascular magnetic resonance imaging.

Background: The rotational direction (RD) of helical blood flow can be classified as either a clockwise (RD) or counter-clockwise (RD) flow. We hypothesized that this simple classification might not be sufficient for analysis and a simultaneous existence of RD may occur. We utilized volumetric velocity-sensitive cardiovascular magnetic resonance imaging (4D flow MRI) to analyze rotational blood flow in the thoracic aorta.

Quantitative 4D flow MRI-derived thoracic aortic normal values of 2D flow MRI parameters in healthy volunteers.

Purpose: To utilize 4 D flow MRI to acquire normal values of "conventional 2 D flow MRI parameters" in healthy volunteers in order to replace multiple single 2 D flow measurements with a single 4 D flow acquisition.

Materials And Methods: A kt-GRAPPA accelerated 4 D flow sequence was used. Flow volumes were assessed by forward (FFV), backward (BFV), and net flow volumes (NFV) [ml/heartbeat] and flow velocities by axial (VAX) and absolute velocity (VABS) [m/s] in 116 volunteers (58 females, 43 ± 13 years).

A Survey of Medical Visualization Through the Lens of Metaphors.

We provide an overview of metaphors that were used in medical visualization and related user interfaces. Metaphors are employed to translate concepts from a source domain to a target domain. The survey is grounded in a discussion of metaphor-based design involving the identification and reflection of candidate metaphors.

Prognostic role of radiomics-based body composition analysis for the 1-year survival for hepatocellular carcinoma patients.

Background: Parameters of body composition have prognostic potential in patients with oncologic diseases. The aim of the present study was to analyse the prognostic potential of radiomics-based parameters of the skeletal musculature and adipose tissues in patients with advanced hepatocellular carcinoma (HCC).

Methods: Radiomics features were extracted from a cohort of 297 HCC patients as post hoc sub-study of the SORAMIC randomized controlled trial.

A teaching concept for school experiments on radioactivity using augmented reality methods.

Digital media are becoming increasingly influential in society, especially among the younger generation. Therefore, an augmented reality (AR) app was developed that simulates experiments with radioactive sources. The app runs experiments on the range and penetration power of alpha, beta and gamma radiation.

Multimodal exploration of the intracranial aneurysm wall.

Purpose: Intracranial aneurysms (IAs) are pathological changes of the intracranial vessel wall, although clinical image data can only show the vessel lumen. Histology can provide wall information but is typically restricted to ex vivo 2D slices where the shape of the tissue is altered.

Methods: We developed a visual exploration pipeline for a comprehensive view of an IA.

A hybrid hierarchical strategy for registration of 7T TOF-MRI to 7T PC-MRI intracranial vessel data.

Purpose: 7T time-of-flight (TOF) MRI provides high resolution for the evaluation of cerebrovascular vessels and pathologies. In combination with 4D flow fields acquired with phase-contrast (PC) MRI, hemodynamic information can be extracted to enhance the analysis by providing direct measurements in the larger arteries or patient-specific boundary conditions. Hence, a registration between both modalities is required.

Deep learning-based semantic vessel graph extraction for intracranial aneurysm rupture risk management.

Purpose: Intracranial aneurysms are vascular deformations in the brain which are complicated to treat. In clinical routines, the risk assessment of intracranial aneurysm rupture is simplified and might be unreliable, especially for patients with multiple aneurysms. Clinical research proposed more advanced analysis of intracranial aneurysm, but requires many complex preprocessing steps.

Interactive visual exploration of surgical process data.

Purpose: Integrated operating rooms provide rich sources of temporal information about surgical procedures, which has led to the emergence of surgical data science. However, little emphasis has been put on interactive visualization of such temporal datasets to gain further insights. Our goal is to put heterogeneous data sequences in relation to better understand the workflows of individual procedures as well as selected subsets, e.

Deep-learning-based Segmentation of Skeletal Muscle Mass in Routine Abdominal CT Scans.

Background: For prediction of many types of clinical outcome, the skeletal muscle mass can be used as an independent biomarker. Manual segmentation of the skeletal muscles is time-consuming, therefore we present a deeplearning-based approach for the identification of muscle mass at the L3 level in clinical routine computed tomographic (CT) data.

Patients And Methods: We conducted a retrospective study of 130 patient datasets.

Quantitative normal values of helical flow, flow jets and wall shear stress of healthy volunteers in the ascending aorta.

Objectives: 4D flow MRI enables quantitative assessment of helical flow. We sought to generate normal values and elucidate changes of helical flow (duration, volume, length, velocities and rotational direction) and flow jet (displacement, flow angle) as well as wall shear stress (WSS).

Methods: We assessed the temporal helical existence (TH), maximum helical volume (HV), accumulated helical volume (HV), accumulated helical volume length (HVL), maximum forward velocity (maxV), maximum circumferential velocity (maxV), rotational direction (RD) and maximum wall shear stress (WSS) as reported elsewhere using the software tool Bloodline in 86 healthy volunteers (46 females, mean age 41 ± 13 years).

A comparison of input devices for precise interaction tasks in VR-based surgical planning and training.

To exploit the potential of virtual reality (VR) in medicine, the input devices must be selected carefully due to their different benefits. In this work, input devices for common interaction tasks in medical VR planning and training are compared. Depending on the specific purpose, different requirements exist.

Self-calibration of C-arm imaging system using interventional instruments during an intracranial biplane angiography.

Purpose: To create an accurate 3D reconstruction of the vascular trees, it is necessary to know the exact geometrical parameters of the angiographic imaging system. Many previous studies used vascular structures to estimate the system's exact geometry. However, utilizing interventional devices and their relative features may be less challenging, as they are unique in different views.

Can Endovascular Treatment of Fusiform Intracranial Aneurysms Restore the Healthy Hemodynamic Environment?-A Virtual Pilot Study.

Numerous studies assess intracranial aneurysm rupture risk based on morphological and hemodynamic parameter analysis in addition to clinical information such as aneurysm localization, age, and sex. However, intracranial aneurysms mostly occur with a saccular shape located either lateral to the parent artery or at a bifurcation. In contrast, fusiform intracranial aneurysms (FIAs), i.

VR-based training of craniotomy for intracranial aneurysm surgery.

Purpose: Intracranial aneurysms can be treated micro-surgically. This procedure involves an appropriate head position of the patient and a proper craniotomy. These steps enable a proper access, facilitating the subsequent steps.

GUCCI - Guided Cardiac Cohort Investigation of Blood Flow Data.

We present the framework GUCCI (Guided Cardiac Cohort Investigation), which provides a guided visual analytics workflow to analyze cohort-based measured blood flow data in the aorta. In the past, many specialized techniques have been developed for the visual exploration of such data sets for a better understanding of the influence of morphological and hemodynamic conditions on cardiovascular diseases. However, there is a lack of dedicated techniques that allow visual comparison of multiple data sets and defined cohorts, which is essential to characterize pathologies.

Virtual embolization for treatment support of intracranial AVMs using an interactive desktop and VR application.

Purpose: The treatment of intracranial arteriovenous malformations (AVM) is challenging due to their complex anatomy. For this vessel pathology, arteries are directly linked to veins without a capillary bed in between. For endovascular treatment, embolization is carried out, where the arteries that supply the AVM are consecutively blocked.

Ten Open Challenges in Medical Visualization.

The medical domain has been an inspiring application area in visualization research for many years already, but many open challenges remain. The driving forces of medical visualization research have been strengthened by novel developments, for example, in deep learning, the advent of affordable VR technology, and the need to provide medical visualizations for broader audiences. At IEEE VIS 2020, we hosted an Application Spotlight session to highlight recent medical visualization research topics.