12,239 research outputs found
Landmark-Free Statistical Shape Modeling Via Neural Flow Deformations
Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shape reconstruction and image segmentation, but also shape generation and classification. Existing shape priors either require dense correspondence between training examples or lack robustness and topological guarantees. We present FlowSSM, a novel shape modeling approach that learns shape variability without requiring dense correspondence between training instances. It relies on a hierarchy of continuous deformation flows, which are parametrized by a neural network. Our model outperforms state-of-the-art methods in providing an expressive and robust shape prior for distal femur and liver. We show that the emerging latent representation is discriminative by separating healthy from pathological shapes. Ultimately, we demonstrate its effectiveness on two shape reconstruction tasks from partial data. Our source code is publicly available (https://github.com/davecasp/flowssm)
Respiratory-induced organ motion compensation for MRgHIFU
Summary: High Intensity Focused Ultrasound is an emerging non-invasive technology for the precise
thermal ablation of pathological tissue deep within the body. The fitful, respiratoryinduced
motion of abdominal organs, such as of the liver, renders targeting challenging.
The work in hand describes methods for imaging, modelling and managing respiratoryinduced
organ motion. The main objective is to enable 3D motion prediction of liver
tumours for the treatment with Magnetic Resonance guided High Intensity Focused Ultrasound
(MRgHIFU).
To model and predict respiratory motion, the liver motion is initially observed in 3D
space. Fast acquired 2D magnetic resonance images are retrospectively reconstructed
to time-resolved volumes, thus called 4DMRI (3D + time). From these volumes, dense
deformation fields describing the motion from time-step to time-step are extracted using
an intensity-based non-rigid registration algorithm. 4DMRI sequences of 20 subjects,
providing long-term recordings of the variability in liver motion under free breathing,
serve as the basis for this study.
Based on the obtained motion data, three main types of models were investigated and
evaluated in clinically relevant scenarios. In particular, subject-specific motion models,
inter-subject population-based motion models and the combination of both are compared
in comprehensive studies. The analysis of the prediction experiments showed that
statistical models based on Principal Component Analysis are well suited to describe
the motion of a single subject as well as of a population of different and unobserved
subjects. In order to enable target prediction, the respiratory state of the respective
organ was tracked in near-real-time and a temporal prediction of its future position is
estimated. The time span provided by the prediction is used to calculate the new target
position and to readjust the treatment focus. In addition, novel methods for faster
acquisition of subject-specific 3D data based on a manifold learner are presented and
compared to the state-of-the art 4DMRI method.
The developed methods provide motion compensation techniques for the non-invasive
and radiation-free treatment of pathological tissue in moving abdominal organs for
MRgHIFU. ---------- Zusammenfassung: High Intensity Focused Ultrasound ist eine aufkommende, nicht-invasive Technologie
für die präzise thermische Zerstörung von pathologischem Gewebe im Körper. Die
unregelmässige ateminduzierte Bewegung der Unterleibsorgane, wie z.B. im Fall der
Leber, macht genaues Zielen anspruchsvoll. Die vorliegende Arbeit beschreibt Verfahren
zur Bildgebung, Modellierung und zur Regelung ateminduzierter Organbewegung.
Das Hauptziel besteht darin, 3D Zielvorhersagen für die Behandlung von Lebertumoren
mittels Magnetic Resonance guided High Intensity Focused Ultrasound
(MRgHIFU) zu ermöglichen.
Um die Atembewegung modellieren und vorhersagen zu können, wird die Bewegung
der Leber zuerst im dreidimensionalen Raum beobachtet. Schnell aufgenommene 2DMagnetresonanz-
Bilder wurden dabei rückwirkend zu Volumen mit sowohl guter zeitlicher
als auch räumlicher Auflösung, daher 4DMRI (3D + Zeit) genannt, rekonstruiert.
Aus diesen Volumen werden Deformationsfelder, welche die Bewegung von Zeitschritt
zu Zeitschritt beschreiben, mit einem intensitätsbasierten, nicht-starren Registrierungsalgorithmus
extrahiert. 4DMRI-Sequenzen von 20 Probanden, welche Langzeitaufzeichungen
von der Variabilität der Leberbewegung beinhalten, dienen als Grundlage für
diese Studie.
Basierend auf den gewonnenen Bewegungsdaten wurden drei Arten von Modellen
in klinisch relevanten Szenarien untersucht und evaluiert. Personen-spezifische Bewegungsmodelle,
populationsbasierende Bewegungsmodelle und die Kombination beider
wurden in umfassenden Studien verglichen. Die Analyse der Vorhersage-Experimente
zeigte, dass statistische Modelle basierend auf Hauptkomponentenanalyse gut geeignet
sind, um die Bewegung einer einzelnen Person sowie einer Population von unterschiedlichen
und unbeobachteten Personen zu beschreiben. Die Bewegungsvorhersage basiert
auf der Abschätzung der Organposition, welche fast in Echtzeit verfolgt wird. Die durch
die Vorhersage bereitgestellte Zeitspanne wird verwendet, um die neue Zielposition zu
berechnen und den Behandlungsfokus auszurichten. Darüber hinaus werden neue Methoden
zur schnelleren Erfassung patienten-spezifischer 3D-Daten und deren Rekonstruktion
vorgestellt und mit der gängigen 4DMRI-Methode verglichen. Die entwickelten Methoden beschreiben Techniken zur nichtinvasiven und strahlungsfreien
Behandlung von krankhaftem Gewebe in bewegten Unterleibsorganen mittels
MRgHIFU
Statistical Shape Modelling and Segmentation of the Respiratory Airway
The human respiratory airway consists of the upper (nasal cavity, pharynx) and the lower (trachea, bronchi) respiratory tracts. Accurate segmentation of these two airway tracts can lead to better diagnosis and interpretation of airway-specific diseases, and lead to improvement in the localization of abnormal metabolic or pathological sites found within and/or surrounding the respiratory regions. Due to the complexity and the variability displayed in the anatomical structure of the upper respiratory airway along with the challenges in distinguishing the nasal cavity from non-respiratory regions such as the paranasal sinuses, it is difficult for existing algorithms to accurately segment the upper airway without manual intervention. This thesis presents an implicit non-parametric framework for constructing a statistical shape model (SSM) of the upper and lower respiratory tract, capable of distinct shape generation and be adapted for segmentation. An SSM of the nasal cavity was successfully constructed using 50 nasal CT scans. The performance of the SSM was evaluated for compactness, specificity and generality. An averaged distance error of 1.47 mm was measured for the generality assessment. The constructed SSM was further adapted with a modified locally constrained random walk algorithm to segment the nasal cavity. The proposed algorithm was evaluated on 30 CT images and outperformed comparative state-of-the-art and conventional algorithms. For the lower airway, a separate algorithm was proposed to automatically segment the trachea and bronchi, and was designed to tolerate the image characteristics inherent in low-contrast CT images. The algorithm was evaluated on 20 clinical low-contrast CT from PET-CT patient studies and demonstrated better performance (87.1±2.8 DSC and distance error of 0.37±0.08 mm) in segmentation results against comparative state-of-the-art algorithms
Statistical deformation reconstruction using multi-organ shape features for pancreatic cancer localization
Respiratory motion and the associated deformations of abdominal organs and tumors are essential information in clinical applications. However, inter- and intra-patient multi-organ deformations are complex and have not been statistically formulated, whereas single organ deformations have been widely studied. In this paper, we introduce a multi-organ deformation library and its application to deformation reconstruction based on the shape features of multiple abdominal organs. Statistical multi-organ motion/deformation models of the stomach, liver, left and right kidneys, and duodenum were generated by shape matching their region labels defined on four-dimensional computed tomography images. A total of 250 volumes were measured from 25 pancreatic cancer patients. This paper also proposes a per-region-based deformation learning using the non-linear kernel model to predict the displacement of pancreatic cancer for adaptive radiotherapy. The experimental results show that the proposed concept estimates deformations better than general per-patient-based learning models and achieves a clinically acceptable estimation error with a mean distance of 1.2 ± 0.7 mm and a Hausdorff distance of 4.2 ± 2.3 mm throughout the respiratory motion
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