Image-guided surgery near anatomical or functional risk structures poses a challenging task for surgeons. To this end, surgical navigation systems that visualize the spatial relation between patient anatomy (represented by 3D images) and surgical instruments have been described. The provided 3D visualizations of these navigation systems are often complex and thus might increase the mental effort for surgeons. Therefore, an appropriate intraoperative visualization of spatial relations between surgical instruments and risk structures poses a pressing need. We propose three visualization methods to improve spatial perception in navigated surgery. A pointer ray encodes the distance between a tracked instrument tip and risk structures along the tool’s main axis. A side-looking radar visualizes the distance between the instrument tip and nearby structures by a ray rotating around the tool. Virtual lighthouses visualize the distances between the instrument tip and predefined anatomical landmarks as color-coded lights flashing between the instrument tip and the landmarks. Our methods aim to encode distance information with low visual complexity. To evaluate our concepts’ usefulness, we conducted a user study with 16 participants. During the study, the participants were asked to insert a pointer tool into a virtual target inside a phantom without touching nearby risk structures or boundaries. Results showed that our concepts were perceived as useful and suitable to improve distance assessment and spatial awareness of risk structures and surgical instruments. Participants were able to safely maneuver the instrument while our navigation cues increased participant confidence of successful avoidance of risk structures.
Thyroid segmentation in tracked 2D ultrasound (US) using active contours has a low segmentation accuracy mainly due to the fact that smaller structures cannot be efficiently recognized and segmented. To address this issue, we propose a new similarity indicator with the main objective to provide information to the active contour algorithm concerning the regions that the active contour should continue to expand or should stop. First, a preprocessing step is carried out in order to attenuate the noise present in the US image and to increase its contrast, using histogram equalization and a median filter. In the second step, active contours are used to segment the thyroid in each 2D image of the dataset. After performing a first segmentation, two similarity indicators (ratio of mean square error, MSE and correlation between histograms) are computed at each contour point of the initial segmented thyroid between rectangles located inside and outside the obtained contour. A threshold is used on a final indicator computed from the other two indicators to find the probable regions for further segmentation using active contours. This process is repeated until no new segmentation region is identified. Finally, all the segmented thyroid images passed through a 3D reconstruction algorithm to obtain a 3D volume segmented thyroid. The results showed that including similarity indicators based on histogram equalization and MSE between inside and outside regions of the contour can help to segment difficult areas that active contours have problem to segment.
The segmentation of the thyroid in ultrasound images is a field of active research. The thyroid is a gland of the endocrine system and regulates several body functions. Measuring the volume of the thyroid is regular practice of diagnosing pathological changes. In this work, we compare three approaches for semi-automatic thyroid segmentation in freehand-tracked three-dimensional ultrasound images. The approaches are based on level set, graph cut and feature classification. For validation, sixteen 3D ultrasound records were created with ground truth segmentations, which we make publicly available. The properties analyzed are the Dice coefficient when compared against the ground truth reference and the effort of required interaction. Our results show that in terms of Dice coefficient, all algorithms perform similarly. For interaction, however, each algorithm has advantages over the other. The graph cut-based approach gives the practitioner direct influence on the final segmentation. Level set and feature classifier require less interaction, but offer less control over the result. All three compared methods show promising results for future work and provide several possible extensions.
Segmentation of hepatic arteries in multi-phase computed tomography (CT) images is indispensable in liver surgery planning. During image acquisition, the hepatic artery is enhanced by the injection of contrast agent. The enhanced signals are often not stably acquired due to non-optimal contrast timing. Other vascular structure, such as hepatic vein or portal vein, can be enhanced as well in the arterial phase, which can adversely affect the segmentation results. Furthermore, the arteries might suffer from partial volume effects due to their small diameter. To overcome these difficulties, we propose a framework for robust hepatic artery segmentation requiring a minimal amount of user interaction. First, an efficient multi-scale Hessian-based vesselness filter is applied on the artery phase CT image, aiming to enhance vessel structures with specified diameter range. Second, the vesselness response is processed using a Bayesian classifier to identify the most probable vessel structures. Considering the vesselness filter normally performs not ideally on the vessel bifurcations or the segments corrupted by noise, two vessel-reconnection techniques are proposed. The first technique uses a directional morphological operator to dilate vessel segments along their centerline directions, attempting to fill the gap between broken vascular segments. The second technique analyzes the connectivity of vessel segments and reconnects disconnected segments and branches. Finally, a 3D vessel tree is reconstructed. The algorithm has been evaluated using 18 CT images of the liver. To quantitatively measure the similarities between segmented and reference vessel trees, the skeleton coverage and mean symmetric distance are calculated to quantify the agreement between reference and segmented vessel skeletons, resulting in an average of 0:55±0:27 and 12:7±7:9 mm (mean standard deviation), respectively.
Computer-aided analysis of venous vasculatures including hepatic veins and portal veins is important in liver surgery planning. The analysis normally consists of two important pre-processing tasks: segmenting both vasculatures and separating them from each other by assigning different labels. During the acquisition of multi-phase CT images, both of the venous vessels are enhanced by injected contrast agent and acquired either in a common phase or in two individual phases. The enhanced signals established by contrast agent are often not stably acquired due to non-optimal acquisition time. Inadequate contrast and the presence of large lesions in oncological patients, make the segmentation task quite challenging. To overcome these diffculties, we propose a framework with minimal user interactions to analyze venous vasculatures in multi-phase CT images. Firstly, presented vasculatures are automatically segmented adopting an efficient multi-scale Hessian-based vesselness filter. The initially segmented vessel trees are then converted to a graph representation, on which a series of graph filters are applied in post-processing steps to rule out irrelevant structures. Eventually, we develop a semi-automatic workow to refine the segmentation in the areas of inferior vena cava and entrance of portal veins, and to simultaneously separate hepatic veins from portal veins. Segmentation quality was evaluated with intensive tests enclosing 60 CT images from both healthy liver donors and oncological patients. To quantitatively measure the similarities between segmented and reference vessel trees, we propose three additional metrics: skeleton distance, branch coverage, and boundary surface distance, which are dedicated to quantifying the misalignment induced by both branching patterns and radii of two vessel trees.
The optimal transfer of preoperative planning data and risk evaluations to the operative site is challenging. A common
practice is to use preoperative 3D planning models as a printout or as a presentation on a display. One important aspect is
that these models were not developed to provide information in complex workspaces like the operating room.
Our aim is to reduce the visual complexity of 3D planning models by mapping surgically relevant information onto a
risk map. Therefore, we present methods for the identification and classification of critical anatomical structures in the
proximity of a preoperatively planned resection surface. Shadow-like distance indicators are introduced to encode the
distance from the resection surface to these critical structures on the risk map. In addition, contour lines are used to
accentuate shape and spatial depth.
The resulting visualization is clear and intuitive, allowing for a fast mental mapping of the current resection surface to
the risk map. Preliminary evaluations by liver surgeons indicate that damage to risk structures may be prevented and
patient safety may be enhanced using the proposed methods.
Tumor resections from the liver are complex surgical interventions. With recent planning software, risk analyses
based on individual liver anatomy can be carried out preoperatively. However, additional tumors within the
liver are frequently detected during oncological interventions using intraoperative ultrasound. These tumors are
not visible in preoperative data and their existence may require changes to the resection strategy. We propose
a novel method that allows an intraoperative risk analysis adaptation by merging newly detected tumors with a
preoperative risk analysis. To determine the exact positions and sizes of these tumors we make use of a navigated
ultrasound-system. A fast communication protocol enables our application to exchange crucial data with this
navigation system during an intervention.
A further motivation for our work is to improve the visual presentation of a moving ultrasound plane within
a complex 3D planning model including vascular systems, tumors, and organ surfaces. In case the ultrasound
plane is located inside the liver, occlusion of the ultrasound plane by the planning model is an inevitable problem
for the applied visualization technique. Our system allows the surgeon to focus on the ultrasound image while
perceiving context-relevant planning information. To improve orientation ability and distance perception, we
include additional depth cues by applying new illustrative visualization algorithms.
Preliminary evaluations confirm that in case of intraoperatively detected tumors a risk analysis adaptation
is beneficial for precise liver surgery. Our new GPU-based visualization approach provides the surgeon with
a simultaneous visualization of planning models and navigated 2D ultrasound data while minimizing occlusion
problems.
The ability to acquire and store radiological images digitally has made this data available to mathematical and scientific
methods. With the step from subjective interpretation to reproducible measurements and knowledge, it is also possible to
develop and apply models that give additional information which is not directly visible in the data. In this context, it is
important to know the characteristics and limitations of each model. Four characteristics assure the clinical relevance of
models for computer-assisted diagnosis and therapy: ability of patient individual adaptation, treatment of errors and
uncertainty, dynamic behavior, and in-depth evaluation. We demonstrate the development and clinical application of a
model in the context of liver surgery. Here, a model for intrahepatic vascular structures is combined with individual, but
in the degree of vascular details limited anatomical information from radiological images. As a result, the model allows
for a dedicated risk analysis and preoperative planning of oncologic resections as well as for living donor liver
transplantations. The clinical relevance of the method was approved in several evaluation studies of our medical partners
and more than 2900 complex surgical cases have been analyzed since 2002.
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