Graph-matching and FEM-based Registration of Computed Tomographies for Outcome Validation of Liver Interventions

Liver cancer is one of the leading causes of death worldwide. One of the reasons for that is the high tumor recurrence rate. The only way to reduce the recurrence rate is to ensure that all carcinogenic cells are destroyed after intervention. Unfortunately, the information available to assess the outcome of an intervention is limited. In the clinical routine, a pair of pre- and post-operatively gathered computed tomographies (CT) of the abdomen are typically compared to decide whether the patient needs further treatment. However, the post-operative liver will be deformed due to breathing and intervention which will complicate the comparison task by simple inspection of both images. The results presented in this thesis will support the physician during the outcome validation process after minimally invasive interventions and open liver surgeries. Therefore, the physician is provided with qualitative measures and visualizations that support him in the decision making task. The basis of a reliable outcome validation is an accurate non-rigid registration method. This thesis proposes to combine internal correspondences at vessel ramifications and landmarks at the surface of the organ to increase the accuracy of the registration results. The internal correspondences are the result of a novel efficient and fully automatic graph matching method. Landmarks at the surface of the liver are given by a method that detects the organs that are adjacent to it at each surface point. Both types of landmarks are incorporated in a FEM-based registration. The registration method has been tested in 25 pairs of pre- and post-operative clinical CT images achieving an average accuracy of 1.22 mm and a positive predictive value of 0.95. In consequence of the accuracy obtained with the proposed methods the physician is able to determine with certainty if the outcome of the intervention was satisfactory. Hence, he can without delay decide to re-treat the patient if needed to remove the remnant tumor. This fast response could at the end reduce the tumor recurrence rate

Graph-matching and FEM-based registration of computed tomographies for outcome validation of liver interventions

Leberkrebs ist eine der größten Todesursachen weltweit. Einer der Gründe dafür ist die hohe Rekurrenz. Um diese zu reduzieren, muss sichergestellt werden, dass alle karzinogenen Zellen nach dem Eingriff zerstört werden. Leider sind die verfügbaren Informationen, um das Resultat eines Eingriffes zu bewerten, begrenzt. Die Entscheidung, ob ein Patient weiter behandelt werden muss, basiert auf dem Vergleich von präoperativen und postoperativen Computertomographien (CT) des Abdomens. Allerdings wird die postoperative Leber aufgrund des Eingriffes und der Atmung stark deformiert, wodurch der visuelle Vergleich erschwert wird. Die Ergebnisse dieser Arbeit unterstützen den Arzt bei der Bewertung minimalinvasiver und offener Lebereingriffe. Dafür werden dem Arzt quantitative Maße und Visualisierungen zur Verfügung gestellt. Eine genaue deformierbare Registrierung bildet die Grundlage für eine zuverlässige Bewertung. Die in dieser Dissertation vorgeschlagene Methode verwendet anatomische Landmarken basierend auf den Gefäßen der Oberfläche der Leber, um eine hohe Genauigkeit zu erreichen. Landmarken innerhalb der Leber werden anhand einer neuartigen, effizienten und voll automatischen Graph-Matching Methode gefunden. Oberflächliche Landmarken werden anhand einer neuen Methode detektiert, die die Leberoberfläche anhand von benachbarten Organen einteilt. Beide Arten von Landmarken wurden in einer FEM basierten Registrierungsmethode integriert. Für die Evaluierung wurden 25 CT Bildpaare bestehend aus jeweils einem prä- und einem postoperativen Datensatz verwendet. Die vorgestellte Methode erreicht eine mittlere Genauigkeit von 1.22 mm und einen positiven Vorhersagewert (PPV) von 0.95. Aufgrund der hohen Genauigkeit der vorgestellten Methode kann der Arzt über den Erfolg des Eingriffes mit einem hohen Maß an Sicherheit entscheiden und den Patienten unverzüglich weiterbehandeln, falls der Tumor nicht vollständig beseitigt wurde. Dadurch kann letztendlich ein Wiederauftreten des Tumors vermieden oder reduziert werden

Anatomical Discovery: Finding Organs in the Neighborhood of the Liver

Image segmentation and registration algorithms are fundamental to assist medical doctors for better treatment of the patients. To this end accuracy in the results given by those algorithms is crucial. The surroundings of the organ to be segmented or registered can provide additional information that at the end improves the result. In this paper a novel algorithm to detect the organs that surround the liver is introduced. Even though our work is focused on the liver, the algorithm could be extended to other parts of the body. The algorithm has been tested in 24 clinical CT datasets. In addition to this, an example application is introduced for which the detection is a useful tool

Generation of a Graph Representation from Three-Dimensional Skeletons of the Liver Vasculature

Extracting hepatic vasculature from three dimensional imagery is important for diagnosis of liver disease and planning of liver surgery. In this paper we propose a method for generation of 3D skeletal graph of liver vessels using thinning algorithm and graph theory. First of all, basic methodology in the proposed method is introduced. Secondly, the skeletonization method together with a pre-processing method on liver vessel images is employed to form liver skeleton image. Thirdly, a graphbased technique is then employed on the skeleton result to efficiently form the hepatic vessel system. The liver vessel tree generation method was evaluated on liver CT datasets to show its effectiveness and efficiency

Two-step FEM-based Liver-CT registration: Improving internal and external accuracy

To know the exact location of the internal structures of the organs, especially the vasculature, is of great importance for the clinicians. This information allows them to know which structures/vessels will be affected by certain therapy and therefore to better treat the patients. However the use of internal structures for registration is often disregarded especially in physical based registration methods. In this paper we propose an algorithm that uses finite element methods to carry out a registration of liver volumes that will not only have accuracy in the boundaries of the organ but also in the interior. Therefore a graph matching algorithm is used to find correspondences between the vessel trees of the two livers to be registered. In addition to this an adaptive volumetric mesh is generated that contains nodes in the locations in which correspondences were found. The displacements derived from those correspondences are the input for the initial deformation of the model. The first deformation brings the internal structures to their final deformed positions and the surfaces close to it. Finally, thin plate splines are used to refine the solution at the boundaries of the organ achieving an improvement in the accuracy of 71%. The algorithm has been evaluated in CT clinical images of the abdomen

Towards Computer Assisted Cardiac Catheterization : How 3D Visualization Supports It

Although cardiac catheterization procedures take place under x-ray guidance, the doctor is almost blind. Vessels are almost invisible until he injects a contrast agent and looking only at 2D x-ray images and reconstructing a 3D image in his head makes it error prone and tedious. Only experienced doctors are able to accomplish this procedure with the expected results. This paper describes our preliminary work and work in progress to support doctors during cardiac catheterizations using 3D visualization

Towards improving cardiac catheterizations through 3D visualization using CT angiography

Our purpose is to assist the catheterization using a preoperatively generated CT angiography to extract the heart, segment the vessels, analyse them and register it intraoperatively with the x-ray angiography to present the "scene" to the doctor in 3D and thus enabling catheterizations also for less experienced doctors

Graph matching survey for medical imaging: On the way to deep learning

The interest on graph matching has not stopped growing since the late seventies. The basic idea of graph matching consists of generating graph representations of different data or structures and compare those representations by searching correspondences between them. There are manifold techniques that have been developed to find those correspondences and the choice of one or another depends on the characteristics of the application of interest. These applications range from pattern recognition (e.g. biometric identification) to signal processing or artificial intelligence. One of the aspects that make graph matching so attractive is its ability to facilitate data analysis, and medical imaging is one of the fields that can benefit from this in a greater extent. The potential of graph matching to find similarities and differences between data acquired at different points in time shows its potential to improve diagnosis, follow-up of human diseases or any other of the clinical scenarios that require comparison between different datasets. In spite of the large amount of papers that were published in this field to the date there is no survey paper of graph matching for clinical applications. This survey aims to fill this gap

Interventional Planning of Liver Resections: An Overview

Liver cancer is the third most common type of cancer. Among available treatment options, a surgical resection offers the best prognosis for long-term survival. It is important that such a surgical procedure is carefully prepared. Modern computer technology offers convenient ways to simulate different resection scenarios and help to determine the best treatment for a given case. This paper provides a non-exhaustive overview of existing computer-based systems for interventional planning of liver resections. They are reviewed according to their medical use case, e.g. if they support typical or atypical resections

Anatomical Discovery: Finding Organs in the Neighborhood of the Liver

Image segmentation and registration algorithms are fundamental to assist medical doctors for better treatment of the patients. To this end accuracy in the results given by those algorithms is crucial. The surroundings of the organ to be segmented or registered can provide additional information that at the end improves the result. In this paper a novel algorithm to detect the organs that surround the liver is introduced. Even though our work is focused on the liver, the algorithm could be extended to other parts of the body. The algorithm has been tested in 24 clinical CT datasets. In addition to this, an example application is introduced for which the detection is a useful tool