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Software Phantoms in Medical Image Analysis

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Software Phantoms in Medical Image Analysis Software Phantoms in Medical Image Analysis D I S S E R T A T I O N zur Erlangung des akademischen Grades Doktoringenieur (Dr.-Ing.) angenommen durch die Fakultät für Informatik der Otto-von-Guericke-Universität Magdeburg von Dipl. Inf. Jan Rexilius geb. am 17.03.1975 in Herdecke Gutachterinnen/Gutachter Prof. Dr. Klaus-Dietz Tönnies Prof. Dr. Simon K. Warfield Prof. Dr. Regina Pohle-Fröhlich Magdeburg, den 16.03.2015 Zusammenfassung Diese Arbeit beschäftigt sich mit der Analyse von Softwarephantomen für die medizinis- che Bildverarbeitung. Dazu werden neue Verfahren in zwei unterschiedlichen Aufgaben- gebieten vorgestellt: (1) Die Entwicklung von Phantomen und deren Anwendung sowie (2) die Validierung von Phantomen. Ein wichtiger erster Schritt für die Entwicklung von Softwarephantomen ist die Betra- chtung der Phantomart und der zugrundeliegenden Anwendung. Die ersten Kapitel dieser Arbeit geben daher zunächst einmal einen Überblick über Designmethoden. Darüber hin- aus werden Parameter untersucht, die man häufig für die Phantomerstellung verwendet. Basierend auf den Ergebnissen dieser Betrachtung erfolgt dann die Entwicklung eines mod- ularen Designs für Phantome. Unser Ziel ist eine Art Baukasten, der eine formalisierte Beschreibung von Parametern nutzt. Dies ermöglicht den Austausch einzelner Parameter durch neue Modelle. Basierend auf unserer Designmethode, entwickeln wir im nächsten Schritt Phantome für unterschiedliche Anwendungen. Besonders die Entwicklung von Multiple Sklerose (MS) Läsionsphantomen sowie von Gehirntumorphantomen stehen im Vordergrund. Dabei wer- den verschiedene Aspekte untersucht, wie etwa das Objektvolumen. Darüber hinaus wird eine makroskopische Simulation von Wachstumsprozessen basierend auf einem physikalisch motivierten, elastischen Modell, vorgeschlagen, das bereits für die Erfassung von Formverän- derungen während neurochirurgischer Eingriffe verwendet wurde. Da für die entwickelten Phantome ein hoher manueller Aufwand notwendig ist, ist die Zahl der verfügbaren Phan- tomdatensätze für eine Anwendung typischerweise sehr klein. In einem weiteren Schritt schlagen wir daher eine vollautomatisierte Methode vor, mit der eine große Zahl an Phan- tomen erstellt werden kann. Die zugehörigen Parametermodelle werden dabei aus Patien- tendaten abgeleitet. Mit Hilfe unseres Baukastenprinzips haben wir zusätzlich einen inter- aktiven Software-Assistenten für die Erstellung von Phantomen implementiert, der den En- twicklungsaufwand für ein Phantom stark reduziert. Eine Reihe von Phantomen aus dieser Arbeit wurden mit dem Assistenten entwickelt. Unsere Phantome bieten eine gute Grundlage für die Evaluierung von Algorithmen, die häufig in der klinischen Routine oder in Studien auftreten. Zum Beispiel analysieren wir die Qualität einer rein visuellen Bewertung von Läsionen im Rahmen einer Studie mit mehr als 20 Teilnehmern. Weiterhin werden neue Ansätze für eine genaue und reproduzierbare i ii Volumetrie von Läsionen untersucht. Der Fokus liegt dabei vor allem auf kleinen Läsionen, wie sie beispielsweise bei Patienten mit MS auftreten. Für die Evaluierung werden mehr als 50 Datensätze mit Läsionsphantomen unterschiedlicher Volumina und Formen erzeugt und manuell von mehreren neuroradiologischen Experten ausgewertet. Darüber hinaus nutzen wir unsere Phantome für die Bewertung von Segmentierungsalgorithmen. Der Schwerpunkt im zweiten Teil der Arbeit liegt auf der Analyse der Phantomqual- ität. Auf Grund der bekannten Ground Truth für alle modellierten Parameter sind Phantome heute ein weit verbreitetes Werkzeug. Trotzdem gibt es praktisch keine Methoden, die eine Bewertung dieser Phantome ermöglichen. Unser erster Schritt in diese Richtung ist daher zunächst einen Untersuchung von Methoden verwandter Gebiete im Bereich der medizinis- chen Bildverarbeitung. Danach wird schließlich ein neues Verfahren für die Validierung von Phantomen vorgestellt, das Verbindungen mit dem Designansatz aus dem ersten Teil dieser Arbeit aufweist. Unsere Methode ermöglicht sowohl eine standardisierte Analyse einzelner Phantome als auch den Vergleich einer Menge von Phantomen. Darüber hinaus wird mit dem sogenannten Analytischen Hierarchieprozess eine Methode verwendet, die eine Bestimmung des Eignungsgrades für alle verwendeten Parameter ermöglicht. Bis ein Phantom eine akzeptable Konfidenz für eine bestimmte Anwendung aufweist sind viele Schritte notwendig. Diesen Aspekt bilden wir auch auf die Phantomvalidierung ab und schlagen einen iterativen Prozess vor, der aus vielen einzelnen Validierungsmetho- den besteht. Den übergeordneten Prozess teilen wir in drei Schritte ein: Methodenauswahl, Methodenvalidierung und Phantomvalidierung. Jede betrachtete Methode vergrößert bzw. verkleinert die Konfidenz in ein Phantom. Dazu müssen zwei Merkmale bewertet werden: die Eignung und die Korrektheit einer Methode. Für die Methodenvalidierung schlagen wir eine Funktion vor, die beide Merkmale miteinander kombiniert. Eine multiplikative Fusion der Ergebnisse aller Methoden liefert schließlich die finale Phantombewertung. Die Qualität eines Phantoms wird mit diesem Ansatz also durch eine Zahl beschrieben. Für eine konkrete Bewertung der vorgestellten Phantomvalidierung werden die MS Lä- sionsphantome aus dem ersten Teil dieser Arbeit verwendet. Fünf verschiedene Validierungs- methoden werden dazu untersucht und kombiniert. Anhand einer Studie mit vier Experten bewerten wir beispielsweise die Detektionsleistung von Läsionsphantomen in Patienten- daten mit echten Läsionen. Eine weitere Methode nutzt den Vergleich von Segmentierungs- ergebnissen zwischen Phantomen und Patientendaten. Die Analyse jeder Methode besteht dabei aus einer kurzen Beschreibung der zugrunde liegenden Daten und einer Diskussion der Ergebnisse. Summary The validation of algorithms is an important aspect in medical image analysis. Today, a validation study serves as a major decision factor of a method’s value to the user. Thereby, phantoms have become a widely accepted tool. This thesis deals with the analysis of soft- ware phantoms in medical image processing. New approaches are proposed in two areas: (1) Phantom development and applications and (2) phantom validation. The first step towards developing a phantom is to decide what kind of phantom is re- quired for the targeted application. Therefore, we start our investigation with a general overview of design approaches and examine parameters used in phantom development. Based on the knowledge of the underlying application domain and the phantom type, we then focus on the actual design. Our goal is a modular phantom design based on a set of components. To this end, we formalize the overall development process and propose mod- els for major parameters used in medical image analysis such as object position, shape, or intensity values. After modeling all relevant parameters for an application, we combine them to our final phantom. Several phantoms are developed using our new design approach. We focus on applica- tions in the field of neurology and neurosurgery, including lesion phantoms that appear in patients with Multiple Sclerosis (MS) as well as brain tumor phantoms. Several character- istic properties are investigated such as the object volume or the selected intensity values. A macroscopic simulation of growth-induced deformations based on a linear elastic model is proposed, which was previously used to capture shape changes of the brain during neu- rosurgery. Instead of developing only a small amount of hand-crafted phantoms, we also present a fully automatic method to build an arbitrary number of MS phantoms. Thereby, our parameter models such as the lesion shape and the lesion position are based on actual patient data. Based on the modular design process, we propose a new interactive software assistant for the development of software phantoms. Several phantoms developed in this work are generated with the software assistant, reducing the time to build a phantom from hours to a few minutes. The developed software phantoms provide an excellent basis for the evaluation of typical algorithms that frequently occur in clinical practice and trials. We investigate the quality of visual assessment in MS lesion volumetry based on a study with more than 20 participants. Furthermore, new methods for an accurate and reproducible volumetry are iii iv presented. Here, the focus is on small lesions that appear in patients with MS. More than 50 software phantoms with different lesion shapes and volumes are generated, and both manual and semi-automatic approaches are analyzed. Finally, our phantoms are used for the evaluation of segmentation methods. Several algorithms ranging from manual to fully- automatic are analyzed. In the second part of this work, we focus on the analysis of the phantom quality. Phan- toms are a widely used tool, since the ground truth is known for all modeled parameters. However, algorithms for phantom validation are widely unknown. Therefore, our first step towards an analysis of the phantom quality consists of a survey of work in related fields of medical image analysis. We then propose an approach that is closely related to the phantom design process. It enables the analysis of a single phantom as well as the comparison of phantoms. A multi-criteria decision making technique is applied to evaluate the suitability of the modeled parameters. Several steps are required to reach a reasonable confidence in a phantom. Therefore, we define phantom validation as an iterative process that comprises several validation methods.
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