649 research outputs found
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
Deep learning approaches have achieved state-of-the-art performance in
cardiac magnetic resonance (CMR) image segmentation. However, most approaches
have focused on learning image intensity features for segmentation, whereas the
incorporation of anatomical shape priors has received less attention. In this
paper, we combine a multi-task deep learning approach with atlas propagation to
develop a shape-constrained bi-ventricular segmentation pipeline for short-axis
CMR volumetric images. The pipeline first employs a fully convolutional network
(FCN) that learns segmentation and landmark localisation tasks simultaneously.
The architecture of the proposed FCN uses a 2.5D representation, thus combining
the computational advantage of 2D FCNs networks and the capability of
addressing 3D spatial consistency without compromising segmentation accuracy.
Moreover, the refinement step is designed to explicitly enforce a shape
constraint and improve segmentation quality. This step is effective for
overcoming image artefacts (e.g. due to different breath-hold positions and
large slice thickness), which preclude the creation of anatomically meaningful
3D cardiac shapes. The proposed pipeline is fully automated, due to network's
ability to infer landmarks, which are then used downstream in the pipeline to
initialise atlas propagation. We validate the pipeline on 1831 healthy subjects
and 649 subjects with pulmonary hypertension. Extensive numerical experiments
on the two datasets demonstrate that our proposed method is robust and capable
of producing accurate, high-resolution and anatomically smooth bi-ventricular
3D models, despite the artefacts in input CMR volumes
Automatic whole heart segmentation based on image registration
Whole heart segmentation can provide important morphological information of the heart, potentially
enabling the development of new clinical applications and the planning and guidance
of cardiac interventional procedures. This information can be extracted from medical images,
such as these of magnetic resonance imaging (MRI), which is becoming a routine modality
for the determination of cardiac morphology. Since manual delineation is labour intensive and
subject to observer variation, it is highly desirable to develop an automatic method. However,
automating the process is complicated by the large shape variation of the heart and limited
quality of the data. The aim of this work is to develop an automatic and robust segmentation
framework from cardiac MRI while overcoming these difficulties.
The main challenge of this segmentation is initialisation of the substructures and inclusion
of shape constraints. We propose the locally affine registration method (LARM) and the freeform
deformations with adaptive control point status to tackle the challenge. They are applied
to the atlas propagation based segmentation framework, where the multi-stage scheme is used to
hierarchically increase the degree of freedom. In this segmentation framework, it is also needed
to compute the inverse transformation for the LARM registration. Therefore, we propose a
generic method, using Dynamic Resampling And distance Weighted interpolation (DRAW), for
inverting dense displacements. The segmentation framework is validated on a clinical dataset
which includes nine pathologies.
To further improve the nonrigid registration against local intensity distortions in the images,
we propose a generalised spatial information encoding scheme and the spatial information
encoded mutual information (SIEMI) registration. SIEMI registration is applied to the segmentation
framework to improve the accuracy. Furthermore, to demonstrate the general applicability
of SIEMI registration, we apply it to the registration of cardiac MRI, brain MRI, and the
contrast enhanced MRI of the liver. SIEMI registration is shown to perform well and achieve
significantly better accuracy compared to the registration using normalised mutual information
Combining Shape and Learning for Medical Image Analysis
Automatic methods with the ability to make accurate, fast and robust assessments of medical images are highly requested in medical research and clinical care. Excellent automatic algorithms are characterized by speed, allowing for scalability, and an accuracy comparable to an expert radiologist. They should produce morphologically and physiologically plausible results while generalizing well to unseen and rare anatomies. Still, there are few, if any, applications where today\u27s automatic methods succeed to meet these requirements.\ua0The focus of this thesis is two tasks essential for enabling automatic medical image assessment, medical image segmentation and medical image registration. Medical image registration, i.e. aligning two separate medical images, is used as an important sub-routine in many image analysis tools as well as in image fusion, disease progress tracking and population statistics. Medical image segmentation, i.e. delineating anatomically or physiologically meaningful boundaries, is used for both diagnostic and visualization purposes in a wide range of applications, e.g. in computer-aided diagnosis and surgery.The thesis comprises five papers addressing medical image registration and/or segmentation for a diverse set of applications and modalities, i.e. pericardium segmentation in cardiac CTA, brain region parcellation in MRI, multi-organ segmentation in CT, heart ventricle segmentation in cardiac ultrasound and tau PET registration. The five papers propose competitive registration and segmentation methods enabled by machine learning techniques, e.g. random decision forests and convolutional neural networks, as well as by shape modelling, e.g. multi-atlas segmentation and conditional random fields
Segmentation of pelvic structures from preoperative images for surgical planning and guidance
Prostate cancer is one of the most frequently diagnosed malignancies globally and the second leading cause of cancer-related mortality in males in the developed world. In recent decades, many techniques have been proposed for prostate cancer diagnosis and treatment. With the development of imaging technologies such as CT and MRI, image-guided procedures have become increasingly important as a means to improve clinical outcomes. Analysis of the preoperative images and construction of 3D models prior to treatment would help doctors to better localize and visualize the structures of interest, plan the procedure, diagnose disease and guide the surgery or therapy. This requires efficient and robust medical image analysis and segmentation technologies to be developed.
The thesis mainly focuses on the development of segmentation techniques in pelvic MRI for image-guided robotic-assisted laparoscopic radical prostatectomy and external-beam radiation therapy. A fully automated multi-atlas framework is proposed for bony pelvis segmentation in MRI, using the guidance of MRI AE-SDM. With the guidance of the AE-SDM, a multi-atlas segmentation algorithm is used to delineate the bony pelvis in a new \ac{MRI} where there is no CT available. The proposed technique outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. With the SDM of pelvis and its segmented surface, an accurate 3D pelvimetry system is designed and implemented to measure a comprehensive set of pelvic geometric parameters for the examination of the relationship between these parameters and the difficulty of robotic-assisted laparoscopic radical prostatectomy. This system can be used in both manual and automated manner with a user-friendly interface.
A fully automated and robust multi-atlas based segmentation has also been developed to delineate the prostate in diagnostic MR scans, which have large variation in both intensity and shape of prostate. Two image analysis techniques are proposed, including patch-based label fusion with local appearance-specific atlases and multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images when limited labeled atlases are available. The proposed techniques can achieve more robust and accurate segmentation results than other multi-atlas based methods.
The seminal vesicles are also an interesting structure for therapy planning, particularly for external-beam radiation therapy. As existing methods fail for the very onerous task of segmenting the seminal vesicles, a multi-atlas learning framework via random decision forests with graph cuts refinement has further been proposed to solve this difficult problem. Motivated by the performance of this technique, I further extend the multi-atlas learning to segment the prostate fully automatically using multispectral (T1 and T2-weighted) MR images via hybrid \ac{RF} classifiers and a multi-image graph cuts technique. The proposed method compares favorably to the previously proposed multi-atlas based prostate segmentation.
The work in this thesis covers different techniques for pelvic image segmentation in MRI. These techniques have been continually developed and refined, and their application to different specific problems shows ever more promising results.Open Acces
Patch-based segmentation with spatial context for medical image analysis
Accurate segmentations in medical imaging form a crucial role in many applications from pa-
tient diagnosis to population studies. As the amount of data generated from medical images
increases, the ability to perform this task without human intervention becomes ever more de-
sirable. One approach, known broadly as atlas-based segmentation, is to propagate labels from
images which have already been manually labelled by clinical experts. Methods using this ap-
proach have been shown to be e ective in many applications, demonstrating great potential for
automatic labelling of large datasets. However, these methods usually require the use of image
registration and are dependent on the outcome of the registration. Any registrations errors
that occur are also propagated to the segmentation process and are likely to have an adverse
e ect on segmentation accuracy. Recently, patch-based methods have been shown to allow a
relaxation of the required image alignment, whilst achieving similar results. In general, these
methods label each voxel of a target image by comparing the image patch centred on the voxel
with neighbouring patches from an atlas library and assigning the most likely label according
to the closest matches. The main contributions of this thesis focuses around this approach
in providing accurate segmentation results whilst minimising the dependency on registration
quality. In particular, this thesis proposes a novel kNN patch-based segmentation framework,
which utilises both intensity and spatial information, and explore the use of spatial context in
a diverse range of applications. The proposed methods extend the potential for patch-based
segmentation to tolerate registration errors by rede ning the \locality" for patch selection and
comparison, whilst also allowing similar looking patches from di erent anatomical structures
to be di erentiated. The methods are evaluated on a wide variety of image datasets, ranging
from the brain to the knees, demonstrating its potential with results which are competitive to
state-of-the-art techniques.Open Acces
Fast left ventricle tracking using localized anatomical affine optical flow
Fast left ventricle tracking using localized anatomical affine optical flowIn daily clinical cardiology practice, left ventricle (LV) global and regional function assessment is crucial for disease diagnosis, therapy selection, and patient follow-up. Currently, this is still a time-consuming task, spending valuable human resources. In this work, a novel fast methodology for automatic LV tracking is proposed based on localized anatomically constrained affine optical flow. This novel method can be combined to previously proposed segmentation frameworks or manually delineated surfaces at an initial frame to obtain fully delineated datasets and, thus, assess both global and regional myocardial function. Its feasibility and accuracy were investigated in 3 distinct public databases, namely in realistically simulated 3D ultrasound, clinical 3D echocardiography, and clinical cine cardiac magnetic resonance images. The method showed accurate tracking results in all databases, proving its applicability and accuracy for myocardial function assessment. Moreover, when combined to previous state-of-the-art segmentation frameworks, it outperformed previous tracking strategies in both 3D ultrasound and cardiac magnetic resonance data, automatically computing relevant cardiac indices with smaller biases and narrower limits of agreement compared to reference indices. Simultaneously, the proposed localized tracking method showed to be suitable for online processing, even for 3D motion assessment. Importantly, although here evaluated for LV tracking only, this novel methodology is applicable for tracking of other target structures with minimal adaptations.The authors acknowledge funding support from FCT - Fundacao para a Ciência e a Tecnologia, Portugal, and
the European Social Found, European Union, through the Programa Operacional Capital Humano (POCH) in
the scope of the PhD grants SFRH/BD/93443/2013 (S. Queiros) and SFRH/BD/95438/2013 (P. Morais), and
by the project ’PersonalizedNOS (01-0145-FEDER-000013)’ co-funded by Programa Operacional Regional
do Norte (Norte2020) through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio
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
An image segmentation and registration approach to cardiac function analysis using MRI
Cardiovascular diseases (CVDs) are one of the major causes of death in the world. In recent
years, significant progress has been made in the care and treatment of patients with such
diseases. A crucial factor for this progress has been the development of magnetic resonance
(MR) imaging which makes it possible to diagnose and assess the cardiovascular function
of the patient. The ability to obtain high-resolution, cine volume images easily and safely
has made it the preferred method for diagnosis of CVDs. MRI is also unique in its ability
to introduce noninvasive markers directly into the tissue being imaged(MR tagging) during
the image acquisition process. With the development of advanced MR imaging acquisition
technologies, 3D MR imaging is more and more clinically feasible. This recent development has
allowed new potentially 3D image analysis technologies to be deployed. However, quantitative
analysis of cardiovascular system from the images remains a challenging topic.
The work presented in this thesis describes the development of segmentation and motion
analysis techniques for the study of the cardiac anatomy and function in cardiac magnetic
resonance (CMR) images. The first main contribution of the thesis is the development of a fully
automatic cardiac segmentation technique that integrates and combines a series of state-of-the-art
techniques. The proposed segmentation technique is capable of generating an accurate 3D
segmentation from multiple image sequences. The proposed segmentation technique is robust
even in the presence of pathological changes, large anatomical shape variations and locally
varying contrast in the images.
Another main contribution of this thesis is the development of motion tracking techniques that
can integrate motion information from different sources. For example, the radial motion of
the myocardium can be tracked easily in untagged MR imaging since the epi- and endocardial
surfaces are clearly visible. On the other hand, tagged MR imaging allows easy tracking of
both longitudinal and circumferential motion. We propose a novel technique based on non-rigid
image registration for the myocardial motion estimation using both untagged and 3D tagged MR
images. The novel aspect of our technique is its simultaneous use of complementary information
from both untagged and 3D tagged MR imaging. The similarity measure is spatially weighted
to maximise the utility of information from both images.
The thesis also proposes a sparse representation for free-form deformations (FFDs) using the principles of compressed sensing. The sparse free-form deformation (SFFD) model can
capture fine local details such as motion discontinuities without sacrificing robustness. We
demonstrate the capabilities of the proposed framework to accurately estimate smooth as well
as discontinuous deformations in 2D and 3D CMR image sequences. Compared to the standard
FFD approach, a significant increase in registration accuracy can be observed in datasets with
discontinuous motion patterns.
Both the segmentation and motion tracking techniques presented in this thesis have been
applied to clinical studies. We focus on two important clinical applications that can be
addressed by the techniques proposed in this thesis. The first clinical application aims
at measuring longitudinal changes in cardiac morphology and function during the cardiac
remodelling process. The second clinical application aims at selecting patients that positively
respond to cardiac resynchronization therapy (CRT).
The final chapter of this thesis summarises the main conclusions that can be drawn from the
work presented here and also discusses possible avenues for future research
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