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.
3D medical images are important components of modern medicine. Their usefulness for the physician depends on their quality, though. Only high-quality images allow accurate and reproducible diagnosis and appropriate support during treatment. We have analyzed 202 MRI images for brain tumor surgery in a retrospective study. Both an experienced neurosurgeon and an experienced neuroradiologist rated each available image with respect to its role in the clinical workflow, its suitability for this specific role, various image quality characteristics, and imaging artifacts. Our results show that MRI data acquired for brain tumor surgery does not always fulfill the required quality standards and that there is a significant disagreement between the surgeon and the radiologist, with the surgeon being more critical. Noise, resolution, as well as the coverage of anatomical structures were the most important criteria for the surgeon, while the radiologist was mainly disturbed by motion artifacts.
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.
Image-guided radiofrequency ablation (RFA) is becoming a standard procedure for minimally invasive tumor
treatment in clinical practice. To verify the treatment success of the therapy, reliable post-interventional assessment
of the ablation zone (coagulation) is essential. Typically, pre- and post-interventional CT images have to
be aligned to compare the shape, size, and position of tumor and coagulation zone. In this work, we present
an automatic workflow for masking liver tissue, enabling a rigid registration algorithm to perform at least as
accurate as experienced medical experts. To minimize the effect of global liver deformations, the registration is
computed in a local region of interest around the pre-interventional lesion and post-interventional coagulation
necrosis. A registration mask excluding lesions and neighboring organs is calculated to prevent the registration
algorithm from matching both lesion shapes instead of the surrounding liver anatomy. As an initial registration
step, the centers of gravity from both lesions are aligned automatically. The subsequent rigid registration method
is based on the Local Cross Correlation (LCC) similarity measure and Newton-type optimization. To assess the
accuracy of our method, 41 RFA cases are registered and compared with the manually aligned cases from four
medical experts. Furthermore, the registration results are compared with ground truth transformations based on
averaged anatomical landmark pairs. In the evaluation, we show that our method allows to automatic alignment
of the data sets with equal accuracy as medical experts, but requiring significancy less time consumption and
variability.
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.
In this work we present a novel approach for elastic image registration of multi-phase contrast enhanced CT
images of liver. A problem in registration of multiphase CT is that the images contain similar but complementary
structures. In our application each image shows a different part of the vessel system, e.g., portal/hepatic
venous/arterial, or biliary vessels. Portal, arterial and biliary vessels run in parallel and abut on each other
forming the so called portal triad, while hepatic veins run independent. Naive registration will tend to align
complementary vessel.
Our new approach is based on minimizing a cost function consisting of a distance measure and a regularizer.
For the distance we use the recently proposed normalized gradient field measure that focuses on the alignment
of edges. For the regularizer we use the linear elastic potential. The key feature of our approach is an additional
penalty term using segmentations of the different vessel systems in the images to avoid overlaps of complementary
structures. We successfully demonstrate our new method by real data examples.
Image guided radiofrequency ablation (RFA) is becoming a standard procedure as a minimally invasive method
for tumor treatment in the clinical routine. The visualization of pathological tissue and potential risk structures
like vessels or important organs gives essential support in image guided pre-interventional RFA planning. In this
work our aim is to present novel visualization techniques for interactive RFA planning to support the physician
with spatial information of pathological structures as well as the finding of trajectories without harming vitally
important tissue. Furthermore, we illustrate three-dimensional applicator models of different manufactures
combined with corresponding ablation areas in homogenous tissue, as specified by the manufacturers, to enhance
the estimated amount of cell destruction caused by ablation. The visualization techniques are embedded in
a workflow oriented application, designed for the use in the clinical routine. To allow a high-quality volume
rendering we integrated a visualization method using the fuzzy c-means algorithm. This method automatically
defines a transfer function for volume visualization of vessels without the need of a segmentation mask. However,
insufficient visualization results of the displayed vessels caused by low data quality can be improved using local
vessel segmentation in the vicinity of the lesion. We also provide an interactive segmentation technique of liver
tumors for the volumetric measurement and for the visualization of pathological tissue combined with anatomical
structures. In order to support coagulation estimation with respect to the heat-sink effect of the cooling blood
flow which decreases thermal ablation, a numerical simulation of the heat distribution is provided.
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.
Image guided radiofrequency (RF) ablation has taken a significant part in the clinical routine as a minimally
invasive method for the treatment of focal liver malignancies. Medical imaging is used in all parts of the clinical
workflow of an RF ablation, incorporating treatment planning, interventional targeting and result assessment.
This paper describes a software application, which has been designed to support the RF ablation workflow under
consideration of the requirements of clinical routine, such as easy user interaction and a high degree of robust and
fast automatic procedures, in order to keep the physician from spending too much time at the computer. The
application therefore provides a collection of specialized image processing and visualization methods for treatment
planning and result assessment. The algorithms are adapted to CT as well as to MR imaging. The planning
support contains semi-automatic methods for the segmentation of liver tumors and the surrounding vascular
system as well as an interactive virtual positioning of RF applicators and a concluding numerical estimation
of the achievable heat distribution. The assessment of the ablation result is supported by the segmentation
of the coagulative necrosis and an interactive registration of pre- and post-interventional image data for the
comparison of tumor and necrosis segmentation masks. An automatic quantification of surface distances is
performed to verify the embedding of the tumor area into the thermal lesion area. The visualization methods
support representations in the commonly used orthogonal 2D view as well as in 3D scenes.
Based on an in-vitro preparation of an adult human lung combined with high-resolution tomography we developed a realistic graph representation of the bronchial tree of a particular human lung. The graph contains topological information about spatial coordinates, connectivities, diameters and branching angles of 1453 bronchi up to the 17th Horsfield order, and is characterized by asymmetric and multifractal properties. This geometrical model was the basis for the development of an unstructured, multiphase CFD model of the trachea and upper airways. This is directly relevant to research in that intricate anatomical system geometries are employed. Based on medical imaging data CFD modeling associated with complex moving geometries, multi-phase/multi-species physics, and turbulence is incorporated. We contrast this approach with the use of mass-transport equations that describe the gas transport axially. Results show that many of the transport processes within the airways depend quite sensitively on the geometry of the bronchial bifurcations and the structure of the boundaries.
KEYWORDS: Lung, Data modeling, Visual process modeling, Gases, Image segmentation, Diffusion, Systems modeling, Medical imaging, Visualization, Nitrogen
Based on clinical CT-data a patient individual model of the bronchial-tree is constructed, incorporating information on irregular dichotomic branching of the airway bifurcations. Combining this geometric model with analytical models of transport and uptake of gas a patient individual outline of ventilation of the lung and the individual time course of inhalation is given. Conjunctions to multi-breath washout analysis are deduced. Central purpose of the presented model is to assess the significance of patient individual local airway geometry on the global ventilation of the lung and the resulting uptake of inhaled gases in the gas exchange regions of the lung.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.