236 research outputs found
Medical image registration by neural networks: a regression-based registration approach
This thesis focuses on the development and evaluation of a registration-by-regression approach for the 3D/2D registration of coronary Computed Tomography Angiography (CTA) and X-ray angiography. This regression-based method relates image features of 2D projection images to the transformation parameters of the 3D image by a nonlinear regression. It treats registration as a regression problem, as an alternative for the traditional iterative approach that often comes with high computational costs and limited capture range.
First we presented a survey of the methods with a regression-based registration approach for medical applications, as well as a summary of their main characteristics (Chapter 2). Second, we studied the registration methodology, addressing the input features and the choice of regression model (Chapter 3 and Chapter 4). For that purpose, we evaluated different options using simulated X-ray images generated from coronary artery tree models derived from 3D CTA scans. We also compared the registration-by-regression results with a method based on iterative optimization. Different image features of 2D projections and seven regression techniques were considered. The regression approach for simulated X-rays was shown to be slightly less accurate, but much more robust than the method based on an iterative optimization approach. Neural Networks obtained accurate results and showed to be robust to large initial misalignment.
Third, we evaluated the registration-by-regression method using clinical data, integrating the 3D preoperative CTA of the coronary arteries with intraoperative 2D X-ray angiography images (Chapter 5). For the evaluation of the image registration, a gold standard registration was established using an exhaustive search followed by a multi-observer visual scoring procedure. The influence of preprocessing options for the simulated images and the real X-rays was studied. Several image features were also compared. The coronary registration–by-regression results were not satisfactory, resembling manual initialization accuracy.
Therefore, the proposed method for this concrete problem and in its current configuration is not sufficiently accurate to be used in the clinical practice. The framework developed enables us to better understand the dependency of the proposed method on the differences between simulated and real images. The main difficulty lies in the substantial differences in appearance between the images used for training (simulated X-rays from 3D coronary models) and the actual images obtained during the intervention (real X-ray angiography). We suggest alternative solutions and recommend to evaluate the registration-by-regression approach in other applications where training data is available that has similar appearance to the eventual test data
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Automatic Spatiotemporal Analysis of Cardiac Image Series
RÉSUMÉ
Ă€ ce jour, les maladies cardiovasculaires demeurent au premier rang des principales causes de
décès en Amérique du Nord. Chez l’adulte et au sein de populations de plus en plus jeunes,
la soi-disant épidémie d’obésité entraînée par certaines habitudes de vie tels que la mauvaise
alimentation, le manque d’exercice et le tabagisme est lourde de conséquences pour les personnes
affectées, mais aussi sur le système de santé. La principale cause de morbidité et de
mortalité chez ces patients est l’athérosclérose, une accumulation de plaque à l’intérieur des
vaisseaux sanguins à hautes pressions telles que les artères coronaires. Les lésions athérosclérotiques
peuvent entraîner l’ischémie en bloquant la circulation sanguine et/ou en provoquant
une thrombose. Cela mène souvent à de graves conséquences telles qu’un infarctus. Outre les
problèmes liés à la sténose, les parois artérielles des régions criblées de plaque augmentent la
rigidité des parois vasculaires, ce qui peut aggraver la condition du patient. Dans la population
pédiatrique, la pathologie cardiovasculaire acquise la plus fréquente est la maladie de
Kawasaki. Il s’agit d’une vasculite aigüe pouvant affecter l’intégrité structurale des parois des
artères coronaires et mener à la formation d’anévrismes. Dans certains cas, ceux-ci entravent
l’hémodynamie artérielle en engendrant une perfusion myocardique insuffisante et en activant
la formation de thromboses.
Le diagnostic de ces deux maladies coronariennes sont traditionnellement effectués à l’aide
d’angiographies par fluoroscopie. Pendant ces examens paracliniques, plusieurs centaines de
projections radiographiques sont acquises en séries suite à l’infusion artérielle d’un agent de
contraste. Ces images révèlent la lumière des vaisseaux sanguins et la présence de lésions
potentiellement pathologiques, s’il y a lieu. Parce que les séries acquises contiennent de l’information
très dynamique en termes de mouvement du patient volontaire et involontaire (ex.
battements cardiaques, respiration et déplacement d’organes), le clinicien base généralement
son interprétation sur une seule image angiographique où des mesures géométriques sont effectuées
manuellement ou semi-automatiquement par un technicien en radiologie. Bien que
l’angiographie par fluoroscopie soit fréquemment utilisé partout dans le monde et souvent
considéré comme l’outil de diagnostic “gold-standard” pour de nombreuses maladies vasculaires,
la nature bidimensionnelle de cette modalité d’imagerie est malheureusement très
limitante en termes de spécification géométrique des différentes régions pathologiques. En effet,
la structure tridimensionnelle des sténoses et des anévrismes ne peut pas être pleinement
appréciée en 2D car les caractéristiques observées varient selon la configuration angulaire de
l’imageur. De plus, la présence de lésions affectant les artères coronaires peut ne pas refléter
la véritable santé du myocarde, car des mécanismes compensatoires naturels (ex. vaisseaux----------ABSTRACT
Cardiovascular disease continues to be the leading cause of death in North America. In adult
and, alarmingly, ever younger populations, the so-called obesity epidemic largely driven by
lifestyle factors that include poor diet, lack of exercise and smoking, incurs enormous stresses
on the healthcare system. The primary cause of serious morbidity and mortality for these
patients is atherosclerosis, the build up of plaque inside high pressure vessels like the coronary
arteries. These lesions can lead to ischemic disease and may progress to precarious blood
flow blockage or thrombosis, often with infarction or other severe consequences. Besides
the stenosis-related outcomes, the arterial walls of plaque-ridden regions manifest increased
stiffness, which may exacerbate negative patient prognosis. In pediatric populations, the
most prevalent acquired cardiovascular pathology is Kawasaki disease. This acute vasculitis
may affect the structural integrity of coronary artery walls and progress to aneurysmal lesions.
These can hinder the blood flow’s hemodynamics, leading to inadequate downstream
perfusion, and may activate thrombus formation which may lead to precarious prognosis.
Diagnosing these two prominent coronary artery diseases is traditionally performed using
fluoroscopic angiography. Several hundred serial x-ray projections are acquired during selective
arterial infusion of a radiodense contrast agent, which reveals the vessels’ luminal
area and possible pathological lesions. The acquired series contain highly dynamic information
on voluntary and involuntary patient movement: respiration, organ displacement and
heartbeat, for example. Current clinical analysis is largely limited to a single angiographic
image where geometrical measures will be performed manually or semi-automatically by a
radiological technician. Although widely used around the world and generally considered
the gold-standard diagnosis tool for many vascular diseases, the two-dimensional nature of
this imaging modality is limiting in terms of specifying the geometry of various pathological
regions. Indeed, the 3D structures of stenotic or aneurysmal lesions may not be fully appreciated
in 2D because their observable features are dependent on the angular configuration of
the imaging gantry. Furthermore, the presence of lesions in the coronary arteries may not
reflect the true health of the myocardium, as natural compensatory mechanisms may obviate
the need for further intervention. In light of this, cardiac magnetic resonance perfusion
imaging is increasingly gaining attention and clinical implementation, as it offers a direct
assessment of myocardial tissue viability following infarction or suspected coronary artery
disease. This type of modality is plagued, however, by motion similar to that present in fluoroscopic
imaging. This issue predisposes clinicians to laborious manual intervention in order
to align anatomical structures in sequential perfusion frames, thus hindering automation o
Coronary Artery Segmentation and Motion Modelling
Conventional coronary artery bypass surgery requires invasive sternotomy and the
use of a cardiopulmonary bypass, which leads to long recovery period and has high
infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery
based on image guided robotic surgical approaches have been developed to allow the
clinicians to conduct the bypass surgery off-pump with only three pin holes incisions
in the chest cavity, through which two robotic arms and one stereo endoscopic camera
are inserted. However, the restricted field of view of the stereo endoscopic images leads
to possible vessel misidentification and coronary artery mis-localization. This results
in 20-30% conversion rates from TECAB surgery to the conventional approach.
We have constructed patient-specific 3D + time coronary artery and left ventricle
motion models from preoperative 4D Computed Tomography Angiography (CTA)
scans. Through temporally and spatially aligning this model with the intraoperative
endoscopic views of the patient's beating heart, this work assists the surgeon to identify
and locate the correct coronaries during the TECAB precedures. Thus this work has
the prospect of reducing the conversion rate from TECAB to conventional coronary
bypass procedures.
This thesis mainly focus on designing segmentation and motion tracking methods
of the coronary arteries in order to build pre-operative patient-specific motion models.
Various vessel centreline extraction and lumen segmentation algorithms are presented,
including intensity based approaches, geometric model matching method and
morphology-based method. A probabilistic atlas of the coronary arteries is formed
from a group of subjects to facilitate the vascular segmentation and registration procedures.
Non-rigid registration framework based on a free-form deformation model
and multi-level multi-channel large deformation diffeomorphic metric mapping are
proposed to track the coronary motion. The methods are applied to 4D CTA images
acquired from various groups of patients and quantitatively evaluated
Curvilinear object segmentation in medical images based on ODoS filter and deep learning network
Automatic segmentation of curvilinear objects in medical images plays an
important role in the diagnosis and evaluation of human diseases, yet it is a
challenging uncertainty in the complex segmentation tasks due to different
issues such as various image appearances, low contrast between curvilinear
objects and their surrounding backgrounds, thin and uneven curvilinear
structures, and improper background illumination conditions. To overcome these
challenges, we present a unique curvilinear structure segmentation framework
based on an oriented derivative of stick (ODoS) filter and a deep learning
network for curvilinear object segmentation in medical images. Currently, a
large number of deep learning models emphasize developing deep architectures
and ignore capturing the structural features of curvilinear objects, which may
lead to unsatisfactory results. Consequently, a new approach that incorporates
an ODoS filter as part of a deep learning network is presented to improve the
spatial attention of curvilinear objects. Specifically, the input image is
transfered into four-channel image constructed by the ODoS filter. In which,
the original image is considered the principal part to describe various image
appearance and complex background illumination conditions, a multi-step
strategy is used to enhance the contrast between curvilinear objects and their
surrounding backgrounds, and a vector field is applied to discriminate thin and
uneven curvilinear structures. Subsequently, a deep learning framework is
employed to extract various structural features for curvilinear object
segmentation in medical images. The performance of the computational model is
validated in experiments conducted on the publicly available DRIVE, STARE and
CHASEDB1 datasets. The experimental results indicate that the presented model
yields surprising results compared with those of some state-of-the-art methods.Comment: 20 pages, 8 figure
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