394 research outputs found
Foetal echocardiographic segmentation
Congenital heart disease affects just under one percentage of all live births [1].
Those defects that manifest themselves as changes to the cardiac chamber volumes
are the motivation for the research presented in this thesis.
Blood volume measurements in vivo require delineation of the cardiac chambers and
manual tracing of foetal cardiac chambers is very time consuming and operator
dependent. This thesis presents a multi region based level set snake deformable
model applied in both 2D and 3D which can automatically adapt to some extent
towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts.
The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD).
The level set methods presented in this thesis have an optional shape prior term for
constraining the segmentation by a template registered to the image in the presence
of shadowing and heavy noise.
When applied to real data in the absence of the template the MSSCD algorithm is
initialised from seed primitives placed at the centre of each cardiac chamber. The
voxel statistics inside the chamber is determined before evolution. The MSSCD stops
at open boundaries between two chambers as the two approaching level set fronts
meet. This has significance when determining volumes for all cardiac compartments
since cardiac indices assume that each chamber is treated in isolation. Comparison
of the segmentation results from the implemented snakes including a previous level
set method in the foetal cardiac literature show that in both 2D and 3D on both real
and synthetic data, the MSSCD formulation is better suited to these types of data.
All the algorithms tested in this thesis are within 2mm error to manually traced
segmentation of the foetal cardiac datasets. This corresponds to less than 10% of
the length of a foetal heart. In addition to comparison with manual tracings all the
amorphous deformable model segmentations in this thesis are validated using a
physical phantom. The volume estimation of the phantom by the MSSCD
segmentation is to within 13% of the physically determined volume
Recommended from our members
Variational segmentation framework in prolate spheroidal coordinates for 3D real-time echocardiography
This paper presents a new formulation of a deformable model segmentation in prolate spheroidal coordinates for segmentation of 3D cardiac echocardiography data. The prolate spheroidal coordinate system enables a representation of the segmented surface with descriptors specifically adapted to the "ellipsoidal" shape of the ventricle. A simple data energy term, based on gray-level information, guides the segmentation. The segmentation framework provides a very fast and simple algorithm to evolve an initial ellipsoidal object towards the endocardial surface of the myocardium with near real-time deformations. With near real-time performance, additional constraints on landmark points, can be used interactively to prevent leakage of the surface
Computer Vision Techniques for Transcatheter Intervention
Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and treatment of cardiovascular diseases. For example, TAVI is an alternative to AVR for the treatment of severe aortic stenosis and TAFA is widely used for the treatment and cure of atrial fibrillation. In addition, catheter-based IVUS and OCT imaging of coronary arteries provides important information about the coronary lumen, wall and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial for the evaluation and treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation, motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods.We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence it is important to understand the application domain, clinical background, and imaging modality so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on background information of transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area
Recommended from our members
State of the Art of Level Set Methods in Segmentation and Registration of Medical Imaging Modalities
Segmentation of medical images is an important step in various applications such as visualization, quantitative analysis and image-guided surgery. Numerous segmentation methods have been developed in the past two decades for extraction of organ contours on medical images. Low-level segmentation methods, such as pixel-based clustering, region growing, and filter-based edge detection, require additional pre-processing and post-processing as well as considerable amounts of expert intervention or information of the objects of interest. Furthermore the subsequent analysis of segmented objects is hampered by the primitive, pixel or voxel level representations from those region-based segmentation. Deformable models, on the other hand, provide an explicit representation of the boundary and the shape of the object. They combine several desirable features such as inherent connectivity and smoothness, which counteract noise and boundary irregularities, as well as the ability to incorporate knowledge about the object of interest. However, parametric deformable models have two main limitations. First, in situations where the initial model and desired object boundary differ greatly in size and shape, the model must be re-parameterized dynamically to faithfully recover the object boundary. The second limitation is that it has difficulty dealing with topological adaptation such as splitting or merging model parts, a useful property for recovering either multiple objects or objects with unknown topology. This difficulty is caused by the fact that a new parameterization must be constructed whenever topology change occurs, which requires sophisticated schemes. Level set deformable models, also referred to as geometric deformable models, provide an elegant solution to address the primary limitations of parametric deformable models. These methods have drawn a great deal of attention since their introduction in 1988. Advantages of the contour implicit formulation of the deformable model over parametric formulation include: (1) no parameterization of the contour, (2) topological flexibility, (3) good numerical stability, (4) straightforward extension of the 2D formulation to n-D. Recent reviews on the subject include papers from Suri. In this chapter we give a general overview of the level set segmentation methods with emphasize on new frameworks recently introduced in the context of medical imaging problems. We then introduce novel approaches that aim at combining segmentation and registration in a level set formulation. Finally we review a selective set of clinical works with detailed validation of the level set methods for several clinical applications
Computer-based estimation of circulating blood volume from ultrasound imagery
Detection of relative changes in circulating blood volume is important to guide resuscitation
and manage a variety of medical conditions including sepsis, trauma, dialysis
and congestive heart failure. In recent years, ultrasound images of inferior vena cava
(IVC) and internal jugular vein (IJV) have been used to assess volume status and
guide fluid administration. This approach has limitations in that a skilled operator
must perform repeated measurements over time.
In this dissertation, we develop semi-automatic image processing algorithms for estimation
and tracking of the IVC anterior-posterior (AP)-diameter and IJV crosssectional
area in ultrasound videos. The proposed algorithms are based on active
contours (ACs), where either the IVC AP-diameter or IJV CSA is estimated by minimization
of an energy functional.
More specifically, in chapter 2, we propose a novel energy functional based on the
third centralized moment and show that it outperforms the functionals that are traditionally
used with active contours (ACs). We combine the proposed functional with
the polar contour representation and use it for segmentation of the IVC.
In chapters 3 and 4, we propose active shape models based on ellipse; circle; and rectangles
fitted inside the IVC as efficient, consistent and novel approaches to tracking
and approximating the anterior-posterior (AP)-diameter even in the context of poor quality images. The proposed algorithms are based on a novel heuristic evolution
functional that works very well with ultrasound images. In chapter 3, we show that
the proposed active circle algorithm accurately, estimates the IVC AP-diameter. Although
the estimated AP-diameter is very close to its actual value, the clinicians define
the IVC AP-diameter as the largest vertical diameter of the IVC contour which deviates
from its actual definition. To solve this problem and estimate the AP-diameter
in the same way as its clinical definition, in chapter 4, we propose the active rectangle
algorithm, where clinically measured AP-diameter is modeled as the height of a vertical
thin rectangle. The results show that the AP-diameter estimated by the active
rectangle algorithm is closer to its clinically measurement than the active circle and
active ellipse algorithms.
In chapter 5, we propose a novel adaptive polar active contour (Ad-PAC) algorithm
for the segmentation and tracking of the IJV in ultrasound videos. In the proposed
algorithm, the parameters of the Ad-PAC algorithm are adapted based on the results
of segmentation in previous frames. The Ad-PAC algorithm has been applied to 65
ultrasound videos and shown to be a significant improvement over existing segmentation
algorithms.
So far, all proposed algorithms are semi-automatic as they need an operator to either
locate the vessel in the first frame, or manually segment the first first and work
automatically for the next frames. In chapter 6, we proposed a novel algorithm to
automatically locate the vessel in ultrasound videos. The proposed algorithm is based
on convolutional neural networks (CNNs) and is trained and applied for IJV videos.
In this chapter we show that although the proposed algorithm is trained for data acquired
from healthy subjects, it works efficiently for the data collected from coronary
heart failure (CHF) patients without additional training.
Finally, conclusions are drawn and possible extensions are discussed in chapter 7
Contributions à la segmentation d'image : phase locale et modèles statistiques
Ce document presente une synthèse de mes travaux apres these, principalement sur la problematique de la segmentation d’images
Image segmentation and reconstruction of 3D surfaces from carotid ultrasound images
Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200
- …