99 research outputs found
Three-dimensional model-based analysis of vascular and cardiac images
This thesis is concerned with the geometrical modeling of organs to perform medical image analysis tasks. The
thesis is divided in two main parts devoted to model linear vessel segments and the left ventricle of the heart,
respectively. Chapters 2 to 4 present different aspects of a model-based technique for semi-automated
quantification of linear vessel segments from 3-D Magnetic Resonance Angiography (MRA). Chapter 2 is
concerned with a multiscale filter for the enhancement of vessels in 2-D and 3-D angiograms. Chapter 3 applies
the filter developed in Chapter 2 to determine the central vessel axis in 3-D MRA images. This procedure is
initialized using an efficient user interaction technique that naturally incorporates the knowledge of the operator
about the vessel of interest. Also in this chapter, a linear vessel model is used to recover the position of the vessel
wall in order to carry out an accurate quantitative analysis of vascular morphology. Prior knowledge is provided
in two main forms: a cylindrical model introduces a shape prior while prior knowledge on the image acquisition
(type of MRA technique) is used to define an appropriate vessel boundary criterion. In Chapter 4 an extensive in
vitro and in vivo evaluation of the algorithm introduced in Chapter 3 is described. Chapters 5 to 7 change the
focus to 3D cardiac image analysis from Magnetic Resonance Imaging. Chapter 5 presents an extensive survey,
a categorization and a critical review of the field of cardiac modeling. Chapter 6 and Chapter 7 present
successive refinements of a method for building statistical models of shape variability with particular emphasis on
cardiac modeling. The method is based on an elastic registration method using hierarchical free-form
deformations. A 3D shape model of the left and right ventricles of the heart was constructed. This model
contains both the average shape of these organs as well as their shape variability. The methodology presented in
the last two chapters could also be applied to other anatomical structures. This has been illustrated in Chapter 6
with examples of geometrical models of the nucleus caudate and the radius
DEFORM'06 - Proceedings of the Workshop on Image Registration in Deformable Environments
Preface These are the proceedings of DEFORM'06, the Workshop on Image Registration in Deformable Environments, associated to BMVC'06, the 17th British Machine Vision Conference, held in Edinburgh, UK, in September 2006. The goal of DEFORM'06 was to bring together people from different domains having interests in deformable image registration. In response to our Call for Papers, we received 17 submissions and selected 8 for oral presentation at the workshop. In addition to the regular papers, Andrew Fitzgibbon from Microsoft Research Cambridge gave an invited talk at the workshop. The conference website including online proceedings remains open, see http://comsee.univ-bpclermont.fr/events/DEFORM06. We would like to thank the BMVC'06 co-chairs, Mike Chantler, Manuel Trucco and especially Bob Fisher for is great help in the local arrangements, Andrew Fitzgibbon, and the Programme Committee members who provided insightful reviews of the submitted papers. Special thanks go to Marc Richetin, head of the CNRS Research Federation TIMS, which sponsored the workshop. August 2006 Adrien Bartoli Nassir Navab Vincent Lepeti
A Survey on Deep Learning in Medical Image Registration: New Technologies, Uncertainty, Evaluation Metrics, and Beyond
Over the past decade, deep learning technologies have greatly advanced the
field of medical image registration. The initial developments, such as
ResNet-based and U-Net-based networks, laid the groundwork for deep
learning-driven image registration. Subsequent progress has been made in
various aspects of deep learning-based registration, including similarity
measures, deformation regularizations, and uncertainty estimation. These
advancements have not only enriched the field of deformable image registration
but have also facilitated its application in a wide range of tasks, including
atlas construction, multi-atlas segmentation, motion estimation, and 2D-3D
registration. In this paper, we present a comprehensive overview of the most
recent advancements in deep learning-based image registration. We begin with a
concise introduction to the core concepts of deep learning-based image
registration. Then, we delve into innovative network architectures, loss
functions specific to registration, and methods for estimating registration
uncertainty. Additionally, this paper explores appropriate evaluation metrics
for assessing the performance of deep learning models in registration tasks.
Finally, we highlight the practical applications of these novel techniques in
medical imaging and discuss the future prospects of deep learning-based image
registration
Fusion and Analysis of Multidimensional Medical Image Data
Analýza medicínských obrazů je předmětem základního výzkumu již řadu let. Za tu dobu bylo v této oblasti publikováno mnoho výzkumných prací zabývajících se dílčími částmi jako je rekonstrukce obrazů, restaurace, segmentace, klasifikace, registrace (lícování) a fúze. Kromě obecného úvodu, pojednává tato disertační práce o dvou medicínsky orientovaných tématech, jež byla formulována ve spolupráci s Philips Netherland BV, divizí Philips Healthcare. První téma je zaměřeno na oblast zpracování obrazů subtrakční angiografie dolních končetin člověka získaných pomocí výpočetní X-Ray tomografie (CT). Subtrakční angiografie je obvykle využívaná při podezření na periferní cévní onemocnění (PAOD) nebo při akutním poškození dolních končetin jako jsou fraktury apod. Současné komerční metody nejsou dostatečně spolehlivé už v předzpracování, jako je například odstranění pacientského stolu, pokrývky, dlahy, apod. Spolehlivost a přesnost identifikace cév v subtrahovaných datech vedoucích v blízkosti kostí je v důsledku Partial Volume artefaktu rovněž nízká. Automatické odstranění kalcifikací nebo detekce malých cév doplňujících nezbytnou informaci o náhradním zásobení dolních končetin krví v případě přerušení hlavních zásobujících cév v současné době rovněž nesplňují kritéria pro plně automatické zpracování. Proto hlavním cílem týkající se tohoto tématu bylo vyvinout automatický systém, který by mohl současné nedostatky v CTSA vyšetření odstranit. Druhé téma je orientováno na identifikaci patologických změn na páteři člověka v CT obrazech se zaměřením na osteolytické a osteoblastické léze u jednotlivých obratlů. Tyto změny obvykle nastávají v důsledků postižení metastazujícím procesem rakovinového onemocnění. Pro detekci patologických změn je pak potřeba identifikace a segmentace jednotlivých obratlů. Přesnost analýzy jednotlivých lézí však závisí rovněž na správné identifikaci těla a zadních segmentů u jednotlivých obratlů a na segmentaci trabekulárního centra obratlů, tj. odstranění kortikální kosti. Během léčby mohou být pacienti skenováni vícekrát, obvykle s několika-mesíčním odstupem. Hodnocení případného vývoje již detekovaných patologických změn pak logicky vychází ze správné detekce patologií v jednotlivých obratlech korespondujících si v jednotlivých akvizicích. Jelikož jsou příslušné obratle v jednotlivých akvizicích obvykle na různé pozici, jejich fúze, vedoucí k analýze časového vývoje detekovaných patologií, je komplikovaná. Požadovaným výsledkem v tomto tématu je vytvoření komplexního systému pro detekci patologických změn v páteři, především osteoblastických a osteolytických lézí. Takový systém tedy musí umožnovat jak segmentaci jednotlivých obratlů, jejich automatické rozdělení na hlavní části a odstranění kortikální kosti, tak také detekci patologických změn a jejich hodnocení. Ačkoliv je tato disertační práce v obou výše zmíněných tématech primárně zaměřena na experimentální část zpracování medicínských obrazů, zabývá se všemi nezbytnými kroky, jako je předzpracování, registrace, dodatečné zpracování a hodnocení výsledků, vedoucími k možné aplikovatelnosti obou systému v klinické praxi. Jelikož oba systémy byly řešeny v rámci týmové spolupráce jako celek, u obou témat jsou pro některé konkrétní kroky uvedeny odkazy na doktorskou práci Miloše Malínského.Analysis of medical images has been subject of basic research for many years. Many research papers have been published in the field related to image analysis and focused on partial aspects such as reconstruction, restoration, segmentation and classification, registration (spatial alignment) and fusion. Besides the introduction of related general concepts used in medical image processing, this thesis deals with two specific medical problems formulated in cooperation with Philips Netherland BV, Philips Healthcare division. The first topic is focused on subtraction angiography in patients’ lower legs utilizing image data from X-Ray computed tomography (CT). CT subtraction angiography (CTSA) is typically used for indication of the Peripheral Artery Occlusive Disease (PAOD) and for examination of acute injuries of lower legs such as acute fractures, etc. Current methods in clinical praxis are not sufficient regarding the pre-processing such as masking of patient desk, cover, splint, etc. The subtraction of blood vessels adjacent to neighboring bones in lower legs is of low accuracy due to the Partial Volume artifact. Masking of calcifications and detection of tiny blood vessels complementing necessary information about the alternative blood supply in lower legs in case of obstruction in main arteries is also not reliable for fully automated process presently. Therefore, the main aim regarding this topic was to develop an automated framework that could overcome current shortcomings in CTSA examination. The second topic is oriented on the identification and evaluation of pathologic changes in human spine, focusing on osteolytic and osteoblastic lesions in individual vertebrae in CT images. Such changes occur typically as a consequence of metastasizing process of cancerous disease. For the detection of pathologic changes, an identification and segmentation of individual vertebrae is necessary. Moreover, the analysis of individual lesions in vertebrae depends also on correct identification of vertebral body and posterior segments of each vertebra, and on segmentation of their trabecular centers. Patients are typically examined more than once during their therapy. Then, the evaluation of possible tumorous progression is based on accurate detection of pathologies in individual vertebrae in the base-line and corresponding follow-up images. Since the corresponding vertebrae are in mutually different positions in the follow-up images, their fusion leading to the analysis of the lesion progression is complicated. The main aim regarding this topic is to develop a complex framework for detection of pathologic lesions on spine, with the main focus on osteoblastic and osteolystic lesions. Such system has to provide not only reliable segmentation of individual vertebrae and detection of their main regions but also the masking of their cortical bone, detection of their pathologic changes and their evaluation. Although this dissertation thesis is primarily oriented at the experimental part of medical image processing considering both the above mentioned topics, it deals with all necessary processing steps, i.e. preprocessing, image registration, post-processing and evaluation of results, leading to the future use of both frameworks in clinical practice. Since both frameworks were developed in a team, there are some chapters referring to the dissertation thesis of Milos Malinsky.
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Fusion of magnetic resonance and ultrasound images for endometriosis detection
Endometriosis is a gynecologic disorder that typically affects women in their reproductive age and is associated with chronic pelvic pain and infertility. In the context of pre-operative diagnosis and guided surgery, endometriosis is a typical example of pathology that requires the use of both magnetic resonance (MR) and ultrasound (US) modalities. These modalities are used side by sidebecause they contain complementary information. However, MRI and US images have different spatial resolutions, fields of view and contrasts and are corrupted by different kinds of noise, which results in important challenges related to their analysis by radiologists. The fusion of MR and US images is a way of facilitating the task of medical experts and improve the pre-operative diagnosis and the surgery mapping. The object of this PhD thesis is to propose a new automatic fusion method for MRI and US images. First, we assume that the MR and US images to be fused are aligned, i.e., there is no geometric distortion between these images. We propose a fusion method for MR and US images, which aims at combining the advantages of each modality, i.e., good contrast and signal to noise ratio for the MR image and good spatial resolution for the US image. The proposed algorithm is based on an inverse problem, performing a super-resolution of the MR image and a denoising of the US image. A polynomial function is introduced to modelthe relationships between the gray levels of the MR and US images. However, the proposed fusion method is very sensitive to registration errors. Thus, in a second step, we introduce a joint fusion and registration method for MR and US images. Registration is a complicated task in practical applications. The proposed MR/US image fusion performs jointly super-resolution of the MR image and despeckling of the US image, and is able to automatically account for registration errors. A polynomial function is used to link ultrasound and MR images in the fusion process while an appropriate similarity measure is introduced to handle the registration problem. The proposed registration is based on a non-rigid transformation containing a local elastic B-spline model and a global affine transformation. The fusion and registration operations are performed alternatively simplifying the underlying optimization problem. The interest of the joint fusion and registration is analyzed using synthetic and experimental phantom images
- …