Deformable Multisurface Segmentation of the Spine for Orthopedic Surgery Planning and Simulation

Purpose: We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data. Approach: This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user assistance is allowed to disable the prior shape influence during deformation. Results: Results demonstrate validation against user-assisted expert segmentation, showing excellent boundary agreement and prevention of spatial overlap between neighboring surfaces. This section also plots the characteristics of the statistical shape model, such as compactness, generalizability and specificity, as a function of the number of modes used to represent the family of shapes. Final results demonstrate a proof-of-concept deformation application based on the open-source surgery simulation Simulation Open Framework Architecture toolkit. Conclusions: To summarize, we present a deformable multisurface model that embeds a shape statistics force, with applications to surgery planning and simulation

Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences

In this article, we present a graph-based method using a cubic template for volumetric segmentation of vertebrae in magnetic resonance imaging (MRI) acquisitions. The user can define the degree of deviation from a regular cube via a smoothness value Delta. The Cube-Cut algorithm generates a directed graph with two terminal nodes (s-t-network), where the nodes of the graph correspond to a cubic-shaped subset of the image's voxels. The weightings of the graph's terminal edges, which connect every node with a virtual source s or a virtual sink t, represent the affinity of a voxel to the vertebra (source) and to the background (sink). Furthermore, a set of infinite weighted and non-terminal edges implements the smoothness term. After graph construction, a minimal s-t-cut is calculated within polynomial computation time, which splits the nodes into two disjoint units. Subsequently, the segmentation result is determined out of the source-set. A quantitative evaluation of a C++ implementation of the algorithm resulted in an average Dice Similarity Coefficient (DSC) of 81.33% and a running time of less than a minute.Comment: 23 figures, 2 tables, 43 references, PLoS ONE 9(4): e9338

Shape/image registration for medical imaging : novel algorithms and applications.

This dissertation looks at two different categories of the registration approaches: Shape registration, and Image registration. It also considers the applications of these approaches into the medical imaging field. Shape registration is an important problem in computer vision, computer graphics and medical imaging. It has been handled in different manners in many applications like shapebased segmentation, shape recognition, and tracking. Image registration is the process of overlaying two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors. Many image processing applications like remote sensing, fusion of medical images, and computer-aided surgery need image registration. This study deals with two different applications in the field of medical image analysis. The first one is related to shape-based segmentation of the human vertebral bodies (VBs). The vertebra consists of the VB, spinous, and other anatomical regions. Spinous pedicles, and ribs should not be included in the bone mineral density (BMD) measurements. The VB segmentation is not an easy task since the ribs have similar gray level information. This dissertation investigates two different segmentation approaches. Both of them are obeying the variational shape-based segmentation frameworks. The first approach deals with two dimensional (2D) case. This segmentation approach starts with obtaining the initial segmentation using the intensity/spatial interaction models. Then, shape model is registered to the image domain. Finally, the optimal segmentation is obtained using the optimization of an energy functional which integrating the shape model with the intensity information. The second one is a 3D simultaneous segmentation and registration approach. The information of the intensity is handled by embedding a Willmore flow into the level set segmentation framework. Then the shape variations are estimated using a new distance probabilistic model. The experimental results show that the segmentation accuracy of the framework are much higher than other alternatives. Applications on BMD measurements of vertebral body are given to illustrate the accuracy of the proposed segmentation approach. The second application is related to the field of computer-aided surgery, specifically on ankle fusion surgery. The long-term goal of this work is to apply this technique to ankle fusion surgery to determine the proper size and orientation of the screws that are used for fusing the bones together. In addition, we try to localize the best bone region to fix these screws. To achieve these goals, the 2D-3D registration is introduced. The role of 2D-3D registration is to enhance the quality of the surgical procedure in terms of time and accuracy, and would greatly reduce the need for repeated surgeries; thus, saving the patients time, expense, and trauma

Multi-Surface Simplex Spine Segmentation for Spine Surgery Simulation and Planning

This research proposes to develop a knowledge-based multi-surface simplex deformable model for segmentation of healthy as well as pathological lumbar spine data. It aims to provide a more accurate and robust segmentation scheme for identification of intervertebral disc pathologies to assist with spine surgery planning. A robust technique that combines multi-surface and shape statistics-aware variants of the deformable simplex model is presented. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user-assistance is allowed to disable the prior shape influence during deformation. Results have been validated against user-assisted expert segmentation

The state-of-the-art in ultrasound-guided spine interventions.

During the last two decades, intra-operative ultrasound (iUS) imaging has been employed for various surgical procedures of the spine, including spinal fusion and needle injections. Accurate and efficient registration of pre-operative computed tomography or magnetic resonance images with iUS images are key elements in the success of iUS-based spine navigation. While widely investigated in research, iUS-based spine navigation has not yet been established in the clinic. This is due to several factors including the lack of a standard methodology for the assessment of accuracy, robustness, reliability, and usability of the registration method. To address these issues, we present a systematic review of the state-of-the-art techniques for iUS-guided registration in spinal image-guided surgery (IGS). The review follows a new taxonomy based on the four steps involved in the surgical workflow that include pre-processing, registration initialization, estimation of the required patient to image transformation, and a visualization process. We provide a detailed analysis of the measurements in terms of accuracy, robustness, reliability, and usability that need to be met during the evaluation of a spinal IGS framework. Although this review is focused on spinal navigation, we expect similar evaluation criteria to be relevant for other IGS applications

Model-based segmentation and registration of multimodal medical images

Ph.DDOCTOR OF PHILOSOPH

Development of ultrasound to measure deformation of functional spinal units in cervical spine

Neck pain is a pervasive problem in the general population, especially in those working in vibrating environments, e.g. military troops and truck drivers. Previous studies showed neck pain was strongly associated with the degeneration of intervertebral disc, which is commonly caused by repetitive loading in the work place. Currently, there is no existing method to measure the in-vivo displacement and loading condition of cervical spine on the site. Therefore, there is little knowledge about the alternation of cervical spine functionality and biomechanics in dynamic environments. In this thesis, a portable ultrasound system was explored as a tool to measure the vertebral motion and functional spinal unit deformation. It is hypothesized that the time sequences of ultrasound imaging signals can be used to characterize the deformation of cervical spine functional spinal units in response to applied displacements and loading. Specifically, a multi-frame tracking algorithm is developed to measure the dynamic movement of vertebrae, which is validated in ex-vivo models. The planar kinematics of the functional spinal units is derived from a dual ultrasound system, which applies two ultrasound systems to image C-spine anteriorly and posteriorly. The kinematics is reconstructed from the results of the multi-frame movement tracking algorithm and a method to co-register ultrasound vertebrae images to MRI scan. Using the dual ultrasound, it is shown that the dynamic deformation of functional spinal unit is affected by the biomechanics properties of intervertebral disc ex-vivo and different applied loading in activities in-vivo. It is concluded that ultrasound is capable of measuring functional spinal units motion, which allows rapid in-vivo evaluation of C-spine in dynamic environments where X-Ray, CT or MRI cannot be used.2020-02-20T00:00:00

Segmentierung medizinischer Bilddaten und bildgestützte intraoperative Navigation

Die Entwicklung von Algorithmen zur automatischen oder semi-automatischen Verarbeitung von medizinischen Bilddaten hat in den letzten Jahren mehr und mehr an Bedeutung gewonnen. Das liegt zum einen an den immer besser werdenden medizinischen Aufnahmemodalitäten, die den menschlichen Körper immer feiner virtuell abbilden können. Zum anderen liegt dies an der verbesserten Computerhardware, die eine algorithmische Verarbeitung der teilweise im Gigabyte-Bereich liegenden Datenmengen in einer vernünftigen Zeit erlaubt. Das Ziel dieser Habilitationsschrift ist die Entwicklung und Evaluation von Algorithmen für die medizinische Bildverarbeitung. Insgesamt besteht die Habilitationsschrift aus einer Reihe von Publikationen, die in drei übergreifende Themenbereiche gegliedert sind: -Segmentierung medizinischer Bilddaten anhand von vorlagenbasierten Algorithmen -Experimentelle Evaluation quelloffener Segmentierungsmethoden unter medizinischen Einsatzbedingungen -Navigation zur Unterstützung intraoperativer Therapien Im Bereich Segmentierung medizinischer Bilddaten anhand von vorlagenbasierten Algorithmen wurden verschiedene graphbasierte Algorithmen in 2D und 3D entwickelt, die einen gerichteten Graphen mittels einer Vorlage aufbauen. Dazu gehört die Bildung eines Algorithmus zur Segmentierung von Wirbeln in 2D und 3D. In 2D wird eine rechteckige und in 3D eine würfelförmige Vorlage genutzt, um den Graphen aufzubauen und das Segmentierungsergebnis zu berechnen. Außerdem wird eine graphbasierte Segmentierung von Prostatadrüsen durch eine Kugelvorlage zur automatischen Bestimmung der Grenzen zwischen Prostatadrüsen und umliegenden Organen vorgestellt. Auf den vorlagenbasierten Algorithmen aufbauend, wurde ein interaktiver Segmentierungsalgorithmus, der einem Benutzer in Echtzeit das Segmentierungsergebnis anzeigt, konzipiert und implementiert. Der Algorithmus nutzt zur Segmentierung die verschiedenen Vorlagen, benötigt allerdings nur einen Saatpunkt des Benutzers. In einem weiteren Ansatz kann der Benutzer die Segmentierung interaktiv durch zusätzliche Saatpunkte verfeinern. Dadurch wird es möglich, eine semi-automatische Segmentierung auch in schwierigen Fällen zu einem zufriedenstellenden Ergebnis zu führen. Im Bereich Evaluation quelloffener Segmentierungsmethoden unter medizinischen Einsatzbedingungen wurden verschiedene frei verfügbare Segmentierungsalgorithmen anhand von Patientendaten aus der klinischen Routine getestet. Dazu gehörte die Evaluierung der semi-automatischen Segmentierung von Hirntumoren, zum Beispiel Hypophysenadenomen und Glioblastomen, mit der frei verfügbaren Open Source-Plattform 3D Slicer. Dadurch konnte gezeigt werden, wie eine rein manuelle Schicht-für-Schicht-Vermessung des Tumorvolumens in der Praxis unterstützt und beschleunigt werden kann. Weiterhin wurde die Segmentierung von Sprachbahnen in medizinischen Aufnahmen von Hirntumorpatienten auf verschiedenen Plattformen evaluiert. Im Bereich Navigation zur Unterstützung intraoperativer Therapien wurden Softwaremodule zum Begleiten von intra-operativen Eingriffen in verschiedenen Phasen einer Behandlung (Therapieplanung, Durchführung, Kontrolle) entwickelt. Dazu gehört die erstmalige Integration des OpenIGTLink-Netzwerkprotokolls in die medizinische Prototyping-Plattform MeVisLab, die anhand eines NDI-Navigationssystems evaluiert wurde. Außerdem wurde hier ebenfalls zum ersten Mal die Konzeption und Implementierung eines medizinischen Software-Prototypen zur Unterstützung der intraoperativen gynäkologischen Brachytherapie vorgestellt. Der Software-Prototyp enthielt auch ein Modul zur erweiterten Visualisierung bei der MR-gestützten interstitiellen gynäkologischen Brachytherapie, welches unter anderem die Registrierung eines gynäkologischen Brachytherapie-Instruments in einen intraoperativen Datensatz einer Patientin ermöglichte. Die einzelnen Module führten zur Vorstellung eines umfassenden bildgestützten Systems für die gynäkologische Brachytherapie in einem multimodalen Operationssaal. Dieses System deckt die prä-, intra- und postoperative Behandlungsphase bei einer interstitiellen gynäkologischen Brachytherapie ab

Investigation of Stereotactic Body Radiation Therapy Delivery Accuracy on an Elekta Linear Accelerator

Purpose: This work investigated the delivery accuracy of high-dose lung and spine stereotactic treatments delivered with the Elekta Infinity and Versa HD platforms. The accuracy of these platforms will be used for consideration in implementing a spine stereotactic radiosurgery (SSRS) program at Mary Bird Perkins Cancer Center. Methods: A geometric phantom was used to perform Winston-Lutz type tests that assessed the relevant degrees of freedom (gantry, collimator, and couch) of the delivery system. A lung stereotactic body radiation therapy (SBRT) and spine SRS treatment plan were generated for use in end-to-end testing. Delivery accuracy was tested using a novel diode array design, which achieved a spatial resolution of 1 mm along a single axis. On board imaging aided in setup of the diode array to the desired position before commencing treatment delivery. The delivered dose distribution and calculated planar dose distributions were compared and analyzed. Several metrics were analyzed from the overlaid profiles, including: percent difference between calculated and measured field centers, and comparison of spatial shifts of the 75% and 60% isodose levels. Percent difference between a calculated and measured point dose quantified discrepancies for the approximate region of the spinal cord. Calculated dose profiles and shifts at the 60 and 75% isodose levels indicated distortions in the profiles. Results: All machines demonstrated an MV Isocenter radius for gantry and treatment table rotation less than 0.70 mm as limited by the Elekta customer acceptance protocol. For SSRS plans, percent difference of the point representing the spinal cord produced results that were consistently higher as a result of a higher dose delivery than calculated. All lung SBRT and SSRS deliveries were capable of achieving an average of 1-mm accuracy. Moreover, profiles showed that the measured profile fell within the planned profile, suggesting a systematic distortion in the profiles. Conclusion: Based on the findings of this project, the Elekta Infinity and Versa HD delivery systems were adequate for lung SBRT treatments but require further exploration for the commencement of spine SRS treatments at Mary Bird Perkins Cancer Center