VCBM: Eurographics Workshop on Visual Computing for Biomedicine

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2.5D Geometric Mapping of Aortic Blood Flow Data for Cohort Visualization
(The Eurographics Association, 2021) Behrendt, Benjamin; Pleuss-Engelhardt, David; Gutberlet, Matthias; Preim, Bernhard; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
Four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) allows for a non-invasive acquisition of timeresolved blood flow measurements, providing a valuable aid to clinicians and researchers seeking a better understanding of the interrelation between pathologies of the cardiovascular system and changes in blood flow patterns. Such research requires extensive analysis and comparison of blood flow data within and between different patient cohorts representing different age groups, genders and pathologies. However, a direct comparison between large numbers of datasets is not feasible due to the complexity of the data. In this paper, we present a novel approach to normalize aortic 4D PC-MRI datasets to enable qualitative and quantitative comparisons. We define normalized coordinate systems for the vessel surface as well as the intravascular volume, allowing for the computation of quantitative measures between datasets for both hemodynamic surface parameters as well as flow or pressure fields. To support the understanding of the geometric deformations involved in this process, individual transformations can not only be toggled on or off, but smoothly transitioned between anatomically faithful and fully abstracted states. In an informal interview with an expert radiologist, we confirm the usefulness of our technique. We also report on initial findings from exploring a database of 138 datasets consisting of both patient and healthy volunteers.
Accelerated Diffusion Operators for Enhancing DW-MRI
(The Eurographics Association, 2010) Rodrigues, Paulo; Duits, Remco; Romeny, Bart M. ter Haar; Vilanova, Anna; Dirk Bartz and Charl Botha and Joachim Hornegger and Raghu Machiraju and Alexander Wiebel and Bernhard Preim
the intra-voxel diffusion pattern compared to its simpler predecessor diffusion tensor imaging (DTI). However, HARDI in general produces very noisy diffusion patterns due to the low SNR from the scanners at high b-values. Furthermore, it still exhibits limitations in areas where the diffusion pattern is asymmetrical (bifurcations, splaying fibers, etc.). To overcome these limitations, enhancement and denoising of the data based on context information is a crucial step. In order to achieve it, convolutions are performed in the coupled spatial and angular domain. Therefore the kernels applied become also HARDI data. However, these approaches have high computational complexity of an already complex HARDI data processing. In this work, we present an accelerated framework for HARDI data regularizaton and enhancement. The convolution operators are optimized by: pre-calculating the kernels, analysing kernels shape and utilizing look-up-tables. We provide an increase of speed, compared to previous brute force approaches of simpler kernels. These methods can be used as a preprocessing for tractography and lead to new ways for investigation of brain white matter.
Adapted Surface Visualization of Cerebral Aneurysms with Embedded Blood Flow Information
(The Eurographics Association, 2010) Gasteiger, Rocco; Neugebauer, Mathias; Kubisch, Christoph; Preim, Bernhard; Dirk Bartz and Charl Botha and Joachim Hornegger and Raghu Machiraju and Alexander Wiebel and Bernhard Preim
Cerebral aneurysms are a vascular dilatation induced by a pathological change of the vessel wall and often require treatment to avoid rupture. Therefore, it is of main interest, to estimate the risk of rupture, to gain a deeper understanding of aneurysm genesis, and to plan an actual intervention, the surface morphology and the internal blood flow characteristics. Visual exploration is primarily used to understand such complex and variable type of data. Since the blood flow data is strongly influenced by the surrounding vessel morphology both have to be visually combined to efficiently support visual exploration. Since the flow is spatially embedded in the surrounding aneurysm surface, occlusion problems have to be tackled. Thereby, a meaningful visual reduction of the aneurysm surface that still provides morphological hints is necessary. We accomplish this by applying an adapted illustrative rendering style to the aneurysm surface. Our contribution lies in the combination and adaption of several rendering styles, which allow us to reduce the problem of occlusion and avoid most of the disadvantages of the traditional semi-transparent surface rendering, like ambiguities in perception of spatial relationships. In interviews with domain experts, we derived visual requirements. Later, we conducted an initial survey with 40 participants (13 medical experts of them), which leads to further improvements of our approach.
Algorithmic Integration and Quantification of Endoscopic and 3D TEE Images in Mitral Valve Surgery
(The Eurographics Association, 2024) Ivantsits, Matthias; Huellebrand, Markus; Walczak, Lars; Welz, Juri; Greve, Dustin; Wamala, Isaac; Suendermann, Simon; Kempfert, Jörg; Falk, Volkmar; Hennemuth, Anja; Garrison, Laura; Jönsson, Daniel
Minimally invasive surgery is the state-of-the-art approach for repairing the mitral valve, which controls the blood flow into the left heart chamber. The surgeons rely on camera and sensor technologies to support visualization, navigation, and measurement. As patients are connected to the cardio-pulmonary bypass, the anatomy is severely deformed by the altered pressure conditions. We developed a technique that combines stereo-endoscopic video with three-dimensional transesophageal echocardiography (3D TEE) to improve anatomic visualization and measurement accuracy during mitral valve repairs. Our methodology includes stereo camera calibration, image segmentation, and 3D model reconstruction. Anatomical landmarks are used to align the imaging modalities. This approach allows the visualization of pre-operatively determined mitral valve properties, e.g., overlaying heat maps in stereo endoscopic data. Our validation results showed high precision and accuracy within an error range of 0.5 ± 0.1 mm. The effectiveness of the heatmap visualization in complex prolapse cases varied. Integrating stereoscopic and 3D TEE promises greater precision in mitral valve repairs. In the future, this approach can also be used to visualize local tissue properties or the optimal locations of implants.
Analyzing Protein Similarity by Clustering Molecular Surface Maps
(The Eurographics Association, 2020) Schatz, Karsten; Frieß, Florian; Schäfer, Marco; Ertl, Thomas; Krone, Michael; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
Many biochemical and biomedical applications like protein engineering or drug design are concerned with finding functionally similar proteins, however, this remains to be a challenging task. We present a new imaged-based approach for identifying and visually comparing proteins with similar function that builds on the hierarchical clustering of Molecular Surface Maps. Such maps are two-dimensional representations of complex molecular surfaces and can be used to visualize the topology and different physico-chemical properties of proteins. Our method is based on the idea that visually similar maps also imply a similarity in the function of the mapped proteins. To determine map similarity we compute descriptive feature vectors using image moments, color moments, or a Convolutional Neural Network and use them for a hierarchical clustering of the maps. We show that image similarity as found by our clustering corresponds to functional similarity of mapped proteins by comparing our results to the BRENDA database, which provides a hierarchical function-based annotation of enzymes. We also compare our results to the TM-score, which is a similarity value for pairs of arbitrary proteins. Our visualization prototype supports the entire workflow from map generation, similarity computing to clustering and can be used to interactively explore and analyze the results.
Anatomical Volume Visualization with Weighted Distance Fields
(The Eurographics Association, 2010) Kerwin, Thomas; Hittle, Brad; Shen, Han-Wei; Stredney, Don; Wiet, Gregory; Dirk Bartz and Charl Botha and Joachim Hornegger and Raghu Machiraju and Alexander Wiebel and Bernhard Preim
We describe the use of the weighted distance transform (WDT) to enhance applications designed for volume visualization of segmented anatomical datasets. The WDT is presented as a general technique to generate a derived characteristic of a scalar field that can be used in multiple ways during rendering. We obtain real-time interaction with the volume by calculating the WDT on the graphics card. Several examples of this technique as it applies to an application for teaching anatomical structures are detailed, including rendering embedded structures, fuzzy boundaries, outlining, and indirect lighting estimation.
Aneulysis - A System for Aneurysm Data Analysis
(The Eurographics Association, 2020) Meuschke, Monique; Wickenhöfer, Ralph; Preim, Bernhard; Lawonn, Kai; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
We present ANEULYSIS, a system to improve risk assessment and treatment planning of cerebral aneurysms. Aneurysm treatment must be carefully examined as there is a risk of fatal outcome during surgery. Aneurysm growth, rupture, and treatment success depend on the interplay of vascular morphology and hemodynamics. Blood flow simulations can obtain the patient-specific hemodynamics. However, analyzing the time-dependent, multi-attribute data is time-consuming and error-prone. ANEULYSIS supports the analysis and visual exploration of aneurysm data including morphological and hemodynamic attributes. Since this is an interdisciplinary process involving both physicians and fluid mechanics experts, we provide a redundancy-free management of aneurysm data sets according to a consistent structure. Major contributions are an improved analysis of morphological aspects, simultaneous evaluation of wall- and flow-related characteristics as well as multiple attributes on the vessel wall, the assessment of mechanical wall processes as well as an automatic classification of the internal flow behavior. It was designed and evaluated in collaboration with domain experts who confirmed its usefulness and clinical necessity.
Annotated Dendrograms for Neurons From the Larval Fruit Fly Brain
(The Eurographics Association, 2018) Strauch, Martin; Hartenstein, Volker; Andrade, Ingrid V.; Cardona, Albert; Merhof, Dorit; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Recent advances in neuroscience have made it possible to reconstruct all neurons of an entire neural circuit in the larval fruit fly brain from serial electron microscopy image stacks. The reconstructed neurons are morphologically complex 3D graphs whose nodes are annotated with labels representing different types of synapses. Here, we propose a method to draw simplified, yet realistic 2D neuron sketches of insect neurons in order to help biologists formulate hypotheses on neural function at the microcircuit level. The sketches are dendrograms that capture a neuron's branching structure and that preserve branch lengths, providing realistic estimates for distances and signal travel times between synapses. To improve readability of the often densely clustered synapse annotations, synapses are automatically summarized in local clusters of synapses of the same type and arranged to minimize label overlap. We show that two major neuron classes of an olfactory circuit in the larval fruit fly brain can be discriminated visually based on the dendrograms. Unsupervised and supervised data analysis reveals that class discrimination can be performed using morphological features derived from the dendrograms.
Aortic Dissection Maps: Comprehensive Visualization of Aortic Dissections for Risk Assessment
(The Eurographics Association, 2016) Mistelbauer, Gabriel; Schmidt, Johanna; Sailer, Anna-Margaretha; Bäumler, Kathrin; Walters, Shannon; Fleischmann, Dominik; Stefan Bruckner and Bernhard Preim and Anna Vilanova and Helwig Hauser and Anja Hennemuth and Arvid Lundervold
Aortic dissection is a life threatening condition of the aorta, characterized by separation of its wall layers into a true and false lumen. A subset of patients require immediate surgical or endovascular repair. All survivors of the acute phase need long-term surveillance with imaging to monitor chronic degeneration and dilatation of the false lumen and prevent late adverse events such as rupture, or malperfusion. We introduce four novel plots displaying features of aortic dissections known or presumed to be associated with risk of future adverse events: Aortic diameter, the blood supply (outflow) to the aortic branches from the true and false lumen, the previous treatment, and an estimate of adverse event-free probabilities in one, two and 5 years. Aortic dissection maps, the composite visualization of these plots, provide a baseline for visual comparison of the complex features and associated risk of aortic dissection. These maps may lead to more individualized monitoring and improved, patient-centric treatment planning in the future.
Application of Image Processing Functions for Brain Tumor Enhancement in Intraoperative Ultrasound Image Data
(The Eurographics Association, 2017) Chalopin, Claire; Mbuyamba, Elisee Ilunga; Aragon, Jesus Guillermo Cabal; Rodriguez, Juan Carlos Camacho; Arlt, Felix; Cervantes, Juan Gabriel Avina; Meixensberger, Juergen; Lindner, Dirk; Stefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Rieder
Intraoperative ultrasound (iUS) imaging supports neurosurgeons significantly during brain tumor operations. At the beginning of the intervention the integration of the iUS image data within the navigation system guides the surgeon by optimally planning the position and size of the skull opening. After tumor resection, the visualization of the iUS image data enables to identify possible tumor residuals. However, the iUS image data can be complex to interpret. Existing segmentation and registration functions were assembled into pipeline to enhance brain tumor contours in the 3D iUS image data. A brain tumor model, semi-automatically segmented in the preoperative MR data of patients, is rigidly registered with the 3D iUS image using image gradient information. The contour of the registered tumor model is visualized on the monitor of the navigation system. The rigid registration step was offline evaluated on 15 patients who overcame a brain tumor operation. The registered tumor models were compared with manual segmentations of the brain tumor in the 3D iUS data. Averaged DSI values of 82.3% and 68.4% and averaged contour mean distances of 1.7 mm and 3.3 mm were obtained for brain metastases and glioblastomas respectively. Future works will include the improvement of the functions in the pipeline, the integration of the pipeline into a centralized assistance system including further fonctionalities and connected with the navigation system, and the evaluation of the system during brain tumor operations.
AR-Assisted Craniotomy Planning for Tumour Resection
(The Eurographics Association, 2021) Wooning, Joost; Benmahdjoub, Mohamed; Walsum, Theo van; Marroquim, Ricardo; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
Craniotomy is a procedure where neurosurgeons open the patient's skull to gain direct access to the brain. The craniotomy's position defines the access path from the skull surface to the tumour and, consequently, the healthy brain tissue to be removed to reach the tumour. This is a complex procedure where a neurosurgeon is required to mentally reconstruct spatial relations of important brain structures to avoid removing them as much as possible. We propose a visualisation method using Augmented Reality to assist in the planning of a craniotomy. The goal of this study is to visualise important brain structures aligned with the physical position of the patient and to allow a better perception of the spatial relations of the structures. Additionally, a heat map was developed that is projected on top of the skull to provide a quick overview of the structures between a chosen location on the skull and the tumour. In the experiments, tracking accuracy was assessed, and colour maps were assessed for use in an AR device. Additionally, we conducted a user study amongst neurosurgeons and surgeons from other fields to evaluate the proposed visualisation using a phantom head. Most participants indeed agree that the visualisation can assist in planning a craniotomy and feedback on future improvements towards the clinical scenario was collected.
Atomistic Visualization of Mesoscopic Whole-Cell Simulations
(The Eurographics Association, 2012) Falk, Martin; Krone, Michael; Ertl, Thomas; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
Molecular visualizations are a principal tool for analyzing the results of biochemical simulations. With modern GPU ray casting approaches it is only possible to render several millions of atoms at interactive frame rates unless advanced acceleration methods are employed. But even simplified cell models of whole-cell simulations consist of at least several billion atoms. However, many instances of only a few different proteins occur in the intracellular environment, which is beneficial in order to fit the data into the graphics memory. One model is stored for each protein species and rendered once per instance. The proposed method exploits recent algorithmic advances for particle rendering and the repetitive nature of intracellular proteins to visualize dynamic results from mesoscopic simulations of cellular transport processes. We present two out-of-core optimizations for the interactive visualization of data sets composed of billions of atoms as well as details on the data preparation and the employed rendering techniques. Furthermore, we apply advanced shading methods to improve the image quality including methods to enhance depth and shape perception besides non-photorealistic rendering methods.
Automated Slice-Based Artery Identification in Various Field-of-View CTA Scans
(The Eurographics Association, 2015) Major, David; Novikov, Alexey A.; Wimmer, Maria; Hladuvka, Jiri; Bühler, Katja; Katja Bühler and Lars Linsen and Nigel W. John
Automated identification of main arteries in Computed Tomography Angiography (CTA) scans plays a key role in the initialization of vessel tracking algorithms. Automated vessel tracking tools support physicians in vessel analysis and make their workflow time-efficient. We present a fully-automated framework for identification of five main arteries of three different body regions in various field-of-view CTA scans. Our method detects the two common iliac arteries, the aorta and the two common carotid arteries and delivers seed positions in them. After the field-of-view of a CTA scan is identified, artery candidate positions are regressed slice-wise and the best candidates are selected by Naive Bayes classification. Final artery seed positions are detected by picking the most optimal path over the artery classification results from slice to slice. Our method was evaluated on 20 CTA scans with various field-of-views. The high detection performance on different arteries shows its generality and future applicability for automated vessel analysis systems.
Automatic Animations to Analyze Blood Flow Data
(The Eurographics Association, 2021) Apilla, Vikram; Behrendt, Benjamin; Lawonn, Kai; Preim, Bernhard; Meuschke, Monique; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
We present an approach for computing camera animations composed of optimal views to support the visual exploration of blood flow data using cerebral aneurysms as major example. Medical researchers are interested in hemodynamic parameters and vessel wall characteristics. The time-dependent character of blood flow data complicates the visual analysis. Our approach is modeled as an optimization problem to automatically determine camera paths during the cardiac cycle. These consist of optimal viewpoints showing regions with suspicious characteristics of wall- and flow-related parameters. This provides medical researchers with an efficient method of obtaining a fast overview of patient-specific blood flow data.
Automatic Cutting and Flattening of Carotid Artery Geometries
(The Eurographics Association, 2021) Eulzer, Pepe; Richter, Kevin; Meuschke, Monique; Hundertmark, Anna; Lawonn, Kai; ,; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
We propose a novel method to cut and flatten vascular geometry that results in an intuitive mapping between the 3D and 2D domains. Our approach is fully automatic, and the sole input is the vessel geometry. We aim to simplify parameter analysis on vessel walls for research on vascular disease and computational hemodynamics. We present a use case for the flattening to aid efforts in investigating the pathophysiology of carotid stenoses (vessel constrictions that are a root cause of stroke). To achieve an intuitive mapping, we introduce the notion of natural vessel cuts. They remain on one side of vessel branches, meaning they adhere to the longitudinal direction and thus result in a comprehensible unfolding of the geometry. Vessel branches and endpoints are automatically detected, and a 2D layout configuration is found that retains the original branch layout. We employ a quasi-isometric surface parameterization to map the geometry to the 2D domain as a single patch. The flattened depiction resolves the need for tedious 3D interaction as the whole surface is visible at once.We found this overview particularly beneficial for exploring temporally resolved parameters.
Automatic Generation of Web-Based User Studies to Evaluate Depth Perception in Vascular Surface Visualizations
(The Eurographics Association, 2018) Meuschke, Monique; Smit, Noeska N.; Lichtenberg, Nils; Preim, Bernhard; Lawonn, Kai; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
User studies are often required in biomedical visualization application papers in order to provide evidence for the utility of the presented approach. An important aspect is how well depth information can be perceived, as depth encoding is important to enable an understandable representation of complex data. Unfortunately, in practice there is often little time available to perform such studies, and setting up and conducting user studies may be labor-intensive. In addition, it can be challenging to reach enough participants to support the contribution claims of the paper. In this paper, we propose a system that allows biomedical visualization researchers to quickly generate perceptual task-based user studies for novel surface visualizations, and to perform the resulting experiment via a web interface. This approach helps to reduce effort in the setup of user studies themselves, and at the same time leverages a web-based approach that can help researchers attract more participants to their study. We demonstrate our system using the specific application of depth judgment tasks to evaluate vascular surface visualizations, since there is a lot of recent interest in this area. However, the system is also generally applicable for conducting other task-based user studies in biomedical visualization.
Automatic Hepatocyte Quantification from Histological Images: Comparing Pre-smoothing filters
(The Eurographics Association, 2008) Ivanovska, Tetyana; Schenk, Andrea; Dahmen, Uta; Hahn, Horst K.; Linsen, Lars; Charl Botha and Gordon Kindlmann and Wiro Niessen and Bernhard Preim
Quantity of hepatocytes in the liver can reveal a lot of information for medical researchers. In our project, it is needed for evaluation of the liver regeneration rate. In this paper, we present a processing pipeline for automatic counting of hepatocytes from images of histological sections. In particular, we propose to introduce a preprocessing step in form of image smoothing. We apply five different smoothing techniques, namely Gaussian smoothing, nonlinear Gaussian smoothing, median filtering, anisotropic diffusion, and minimum description length segmentation, and compare them to each other. The processing pipeline is completed by subsequent automatic thresholding using Otsu s method and hepatocyte detection using Hough transform. We compare the quantification results in terms of quality (sensitivity and specificity rates) against the manually specified ground truth. We discuss the results and limitations of the individual processing steps as well as of the overall automatic quantification approach.
Automatic Real-time Annotation of Important Landmarks in Ultrasound-Guided Femoral Nerve Blocks
(The Eurographics Association, 2015) Lindseth, Frank; Leidig, Linda; Smistad, Erik; Katja Bühler and Lars Linsen and Nigel W. John
The main focus of the preliminary work presented here is the automatic real-time annotation (detection and tracking) of the important structures seen in an ultrasound image taken from a femoral nerve block, i.e. the femoral artery, the facias (lata and illiaca) and the femoral nerve.
Automatic Segmentation of the Pelvic Bones from CT Data Based on a Statistical Shape Model
(The Eurographics Association, 2008) Seim, Heiko; Kainmueller, Dagmar; Heller, Markus; Lamecker, Hans; Zachow, Stefan; Hege, Hans-Christian; Charl Botha and Gordon Kindlmann and Wiro Niessen and Bernhard Preim
We present an algorithm for automatic segmentation of the human pelvic bones from CT datasets that is based on the application of a statistical shape model. The proposed method is divided into three steps: 1) The averaged shape of the pelvis model is initially placed within the CT data using the Generalized Hough Transform, 2) the statistical shape model is then adapted to the image data by a transformation and variation of its shape modes, and 3) a final free-form deformation step based on optimal graph searching is applied to overcome the restrictive character of the statistical shape representation. We thoroughly evaluated the method on 50 manually segmented CT datasets by performing a leave-one-out study. The Generalized Hough Transform proved to be a reliable method for an automatic initial placement of the shape model within the CT data. Compared to the manual gold standard segmentations, our automatic segmentation approach produced an average surface distance of 1.2 ± 0.3mm after the adaptation of the statistical shape model, which could be reduced to 0.7±0.3mm using a final free-form deformation step. Together with an average segmentation time of less than 5 minutes, the results of our study indicate that our method meets the requirements of clinical routine.
Automatic Thrombus Detection in Non-enhanced Computed Tomography Images in Patients With Acute Ischemic Stroke
(The Eurographics Association, 2017) Löber, Patrick; Stimpel, Bernhard; Syben, Christopher; Maier, Andreas; Ditt, Hendrik; Schramm, Peter; Raczkowski, Boy; Kemmling, André; Stefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Rieder
In case of an ischemic stroke, identifying and removing blood clots is crucial for a successful recovery. We present a novel method to automatically detect vascular occlusion in non-enhanced computed tomography (NECT) images. Possible hyperdense thrombus candidates are extracted by thresholding and connected component clustering. A set of different features is computed to describe the objects, and a Random Forest classifier is applied to predict them. Thrombus classification yields 98.7% sensitivity with 6.7 false positives per volume, and 91.1% sensitivity with 2.7 false positives per volume. The classifier assigns a clot probability > = 90% for every thrombus with a volume larger than 100 mm3 or with a length above 23 mm, and can be used as a reliable method to detect blood clots.

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