VCBM 17: Eurographics Workshop on Visual Computing for Biology and Medicine

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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.
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.
Bone Fracture and Lesion Assessment using Shape-Adaptive Unfolding
(The Eurographics Association, 2017) Martinke, Hannes; Petry, Christian; Großkopf, Stefan; Suehling, Michael; Soza, Grzegorz; Preim, Bernhard; Mistelbauer, Gabriel; Stefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Rieder
The assessment of rib bone fractures and lesions consists of many images that have to be thoroughly inspected slice-by-slice and rib-by-rib. Existing visualization methods, such as curved planar reformation (CPR), reduce the number of images to inspect and, in turn, the time spent per case. However, this task remains time-consuming and exhausting. In this paper, we propose a novel rib unfolding strategy that considers the cross-sectional shape of each rib individually and independently. This leads to shape-adaptive slices through the ribs. By aggregating these slices into a single image, we support radiologists with a concise overview visualization of the entire rib cage for fracture and lesion assessment. We present results of our approach along different cases of rib and spinal fractures as well as lesions. To assess the applicability of our method, we separately evaluated the segmentation (with 954 data sets) and the visualization (with two clinical coaches).
Comparative Visualization for Diffusion Tensor Imaging Group Study at Multiple Levels of Detail
(The Eurographics Association, 2017) Zhang, Changgong; Höllt, Thomas; Caan, Matthan W. A.; Eisemann, Elmar; Vilanova, Anna; Stefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Rieder
Diffusion Tensor Imaging (DTI) group studies often require the comparison of two groups of 3D diffusion tensor fields. The total number of datasets involved in the study and the multivariate nature of diffusion tensors together make this a challenging process. The traditional approach is to reduce the six-dimensional diffusion tensor to some scalar quantities, which can be analyzed with univariate statistical methods, and visualized with standard techniques such as slice views. However, this provides merely part of the whole story due to information reduction. If to take the full tensor information into account, only few methods are available, and they focus on the analysis of a single group, rather than the comparison of two groups. Simultaneously comparing two groups of diffusion tensor fields by simple juxtaposition or superposition is rather impractical. In this work, we extend previous work by Zhang et al. [ZCH 17] to visually compare two groups of diffusion tensor fields. To deal with the wealth of information, the comparison is carried out at multiple levels of detail. In the 3D spatial domain, we propose a detailson- demand glyph representation to support the visual comparison of the tensor ensemble summary information in a progressive manner. The spatial view guides analysts to select voxels of interest. Then at the detail level, the respective original tensor ensembles are compared in terms of tensor intrinsic properties, with special care taken to reduce visual clutter. We demonstrate the usefulness of our visual analysis system by comparing a control group and an HIV positive patient group.
CT-Based Navigation Guidance for Liver Tumor Ablation
(The Eurographics Association, 2017) Alpers, Julian; Hansen, Christian; Ringe, Kristina; Rieder, Christian; Stefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Rieder
Image-guided thermal ablation procedures such as microwave ablation (MWA) or radiofrequency ablation (RFA) have become clinically accepted treatment options for liver tumors. The goal of these minimally invasive procedures is the destruction of focal liver malignancies using mostly needle-shaped instruments. Computed tomography (CT) imaging may be used to navigate the applicator to the target position in order to achieve complete tumor ablation. Due to limited image quality and resolution, the treatment target and risk structures may be hardly visible in intra-interventional CT-images, hampering verification of the intended applicator position. In this work, we propose a navigation guidance method based only on CT images to support the physician with additional information to reach the target position. Therefore, planning information extracted from pre-interventional images is fused with the current intra-interventional image. The visible applicator is extracted semi-automatically from the intra-interventional image. The localization of the needle instrument is used to guide the physician by display of the pathway, projection of anatomical structures, and correction suggestions. In an evaluation, we demonstrate the potential of the proposed method to improve the clinical success rate of complex liver tumor ablations while increasing the accuracy and reducing the number of intra-interventional CT images needed.
Exploration of Interventricular Septum Motion in Multi-Cycle Cardiac MRI
(The Eurographics Association, 2017) Tautz, Lennart; Hüllebrand, Markus; Steinmetz, Michael; Voit, Dirk; Frahm, Jens; Hennemuth, Anja; Stefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Rieder
Function of the heart, including interventricular septum motion, is influenced by respiration and contraction of the heart muscle. Recent real-time magnetic resonance imaging (MRI) can acquire multi-cycle cardiac data, which enables the analysis of the variation between heart cycles depending on factors such as physical stress or changes in respiration. There are no normal values for this variation in the literature, and there are no established tools for the analysis and exploration of such multi-cycle data available. We propose an analysis and exploration concept that automatically segments the left and right ventricle, extracts motion parameters and allows to interactively explore the results. We tested the concept using nine real-time MRI data sets, including one subject under increasing stress levels and one subject performing a breathing maneuver. All data sets could be automatically processed and then explored successfully, suggesting that our approach can robustly quantify and explore septum thickness in real-time MRI data.
A Guided Spatial Transformer Network for Histology Cell Differentiation
(The Eurographics Association, 2017) Aubreville, Marc; Krappmann, Maximilian; Bertram, Christof; Klopfleisch, Robert; Maier, Andreas; Stefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Rieder
Identification and counting of cells and mitotic figures is a standard task in diagnostic histopathology. Due to the large overall cell count on histological slides and the potential sparse prevalence of some relevant cell types or mitotic figures, retrieving annotation data for sufficient statistics is a tedious task and prone to a significant error in assessment. Automatic classification and segmentation is a classic task in digital pathology, yet it is not solved to a sufficient degree. We present a novel approach for cell and mitotic figure classification, based on a deep convolutional network with an incorporated Spatial Transformer Network. The network was trained on a novel data set with ten thousand mitotic figures, about ten times more than previous data sets. The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image. The mean accuracy of the algorithm in a five-fold cross-validation is 91.45 %. In our view, the approach is a promising step into the direction of a more objective and accurate, semi-automatized mitosis counting supporting the pathologist.
MRI Hip Joint Segmentation: A Locally Bhattacharyya Weighted Hybrid 3D Level Set Approach
(The Eurographics Association, 2017) Pham, Duc Duy; Morariu, Cosmin Adrian; Terheiden, Tobias; Landgraeber, Stefan; Jäger, Marcus; Pauli, Josef; Stefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Rieder
In this paper, we propose a novel hybrid level set approach that locally balances the combined use of both Gradient Vector Flow and region based energy cost function by means of the Bhattacharyya coefficient. The local neighborhood of each contour point is naturally divided into an area encapsulated and one excluded by the contour. We propose utilizing the Bhattacharyya coefficient of the intensity distributions of these local areas to determine a point-wise weighting scheme for the curve propagation. The performance of our method regarding segmentation quality is evaluated on the segmentation of the hip joint in 10 MRI data sets. Our proposed method shows a clear improvement compared to conventional 3D level set approaches.
UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model
(The Eurographics Association, 2017) Amrehn, Mario; Gaube, Sven; Unberath, Mathias; Schebesch, Frank; Horz, Tim; Strumia, Maddalena; Steidl, Stefan; Kowarschik, Markus; Maier, Andreas; Stefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Rieder
For complex segmentation tasks, fully automatic systems are inherently limited in their achievable accuracy for extracting relevant objects. Especially in cases where only few data sets need to be processed for a highly accurate result, semi-automatic segmentation techniques exhibit a clear benefit for the user. One area of application is medical image processing during an intervention for a single patient.We propose a learning-based cooperative segmentation approach which includes the computing entity as well as the user into the task. Our system builds upon a state-of-the-art fully convolutional artificial neural network (FCN) as well as an active user model for training. During the segmentation process, a user of the trained system can iteratively add additional hints in form of pictorial scribbles as seed points into the FCN system to achieve an interactive and precise segmentation result. The segmentation quality of interactive FCNs is evaluated. Iterative FCN approaches can yield superior results compared to networks without the user input channel component, due to a consistent improvement in segmentation quality after each interaction.
Visual Navigation Support for Liver Applicator Placement using Interactive Map Displays
(The Eurographics Association, 2017) Hettig, Julian; Mistelbauer, Gabriel; Rieder, Christian; Lawonn, Kai; Hansen, Christian; Stefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Rieder
Navigated placement of an ablation applicator in liver surgery would benefit from an effective intraoperative visualization of delicate 3D anatomical structures. In this paper, we propose an approach that facilitates surgery with an interactive as well as an animated map display to support navigated applicator placement in the liver. By reducing the visual complexity of 3D anatomical structures, we provide only the most important information on and around a planned applicator path. By employing different illustrative visualization techniques, the applicator path and its surrounding critical structures, such as blood vessels, are clearly conveyed in an unobstructed way. To retain contextual information around the applicator path and its tip, we desaturate these structures with increasing distance. To alleviate time-consuming and tedious interaction during surgery, our visualization is controlled solely by the position and orientation of a tracked applicator. This enables a direct interaction with the map display without interruption of the intervention. Based on our requirement analysis, we conducted a pilot study with eleven participants and an interactive user study with six domain experts to assess the task completion time, error rate, visual parameters and the usefulness of the animation. The outcome of our pilot study shows that our map display facilitates significantly faster decision making (11.8 s vs. 40.9 s) and significantly fewer false assessments of structures at risk (7.4 % vs. 10.3 %) compared to a currently employed 3D visualization. Furthermore, the animation supports timely perception of the course and depth of upcoming blood vessels, and helps to detect possible areas at risk along the path in advance. Hence, the obtained results demonstrate that our proposed interactive map displays exhibit potential to improve the outcome of navigated liver interventions.
Visualizing and Exploring Dynamic Multichannel EEG Coherence Networks
(The Eurographics Association, 2017) Ji, Chengtao; Gronde, Jasper J. van de; Maurits, Natasha M.; Roerdink, Jos B. T. M.; Stefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Rieder
An electroencephalography (EEG) coherence network represents functional brain connectivity, and is constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of coherence networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists to understand brain mechanisms. However, visualizing dynamic EEG coherence networks is a challenge for the analysis of brain connectivity, especially when the spatial structure of the network needs to be taken into account. In this paper, we present a design and implementation of a visualization framework for such dynamic networks. First, requirements for supporting typical tasks in the context of dynamic functional connectivity network analysis were collected from neuroscience researchers. In our design, we consider groups of network nodes and their corresponding spatial location for visualizing the evolution of the dynamic coherence network. We introduce an augmented timeline-based representation to provide an overview of the evolution of functional units (FUs) and their spatial location over time. This representation can help the viewer to identify relations between functional connectivity and brain regions, as well as to identify persistent or transient functional connectivity patterns across the whole timewindow. In addition, we modified the FU map representation to facilitate comparison of the behavior of nodes between consecutive FU maps. Our implementation also supports interactive exploration. The usefulness of our visualization design was evaluated by an informal user study. The feedback we received shows that our design supports exploratory analysis tasks well. The method can serve as an preprocessing step before a complete analysis of dynamic EEG coherence networks.
A Web-Based Tool for Cardiac Dyssynchrony Assessment on Ultrasound Data
(The Eurographics Association, 2017) Pezzatini, Daniele; Yagüe, Carlos; Rudenick, Paula; Blat, Josep; Bijnens, Bart; Camara, Oscar; Stefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Rieder
Cardiac resynchronization therapy (CRT) is a broadly used therapy in patients that suffers from heart failure (HF). The positive outcome of CRT depends strongly on the parameters criteria used to select patients and a lot of research has been done to introduce new and more reliable parameters. In this paper we propose an interactive tool to perform visual assessment and measurements on cardiac ultrasound images of patient with cardiac dyssynchrony. The tool is developed as a web application, allowing doctors to remotely access images and measurements.

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