106 research outputs found
Novel Approaches to the Representation and Analysis of 3D Segmented Anatomical Districts
Nowadays, image processing and 3D shape analysis are an integral part of clinical
practice and have the potentiality to support clinicians with advanced analysis
and visualization techniques. Both approaches provide visual and quantitative information
to medical practitioners, even if from different points of view. Indeed,
shape analysis is aimed at studying the morphology of anatomical structures, while
image processing is focused more on the tissue or functional information provided
by the pixels/voxels intensities levels. Despite the progress obtained by research in
both fields, a junction between these two complementary worlds is missing. When
working with 3D models analyzing shape features, the information of the volume
surrounding the structure is lost, since a segmentation process is needed to obtain
the 3D shape model; however, the 3D nature of the anatomical structure is represented
explicitly. With volume images, instead, the tissue information related to the
imaged volume is the core of the analysis, while the shape and morphology of the
structure are just implicitly represented, thus not clear enough.
The aim of this Thesis work is the integration of these two approaches in order to increase
the amount of information available for physicians, allowing a more accurate
analysis of each patient. An augmented visualization tool able to provide information
on both the anatomical structure shape and the surrounding volume through a
hybrid representation, could reduce the gap between the two approaches and provide
a more complete anatomical rendering of the subject.
To this end, given a segmented anatomical district, we propose a novel mapping of
volumetric data onto the segmented surface. The grey-levels of the image voxels are
mapped through a volume-surface correspondence map, which defines a grey-level
texture on the segmented surface. The resulting texture mapping is coherent to the
local morphology of the segmented anatomical structure and provides an enhanced
visual representation of the anatomical district. The integration of volume-based and
surface-based information in a unique 3D representation also supports the identification
and characterization of morphological landmarks and pathology evaluations.
The main research contributions of the Ph.D. activities and Thesis are:
\u2022 the development of a novel integration algorithm that combines surface-based
(segmented 3D anatomical structure meshes) and volume-based (MRI volumes)
information. The integration supports different criteria for the grey-levels mapping
onto the segmented surface;
\u2022 the development of methodological approaches for using the grey-levels mapping
together with morphological analysis. The final goal is to solve problems
in real clinical tasks, such as the identification of (patient-specific) ligament
insertion sites on bones from segmented MR images, the characterization of
the local morphology of bones/tissues, the early diagnosis, classification, and
monitoring of muscle-skeletal pathologies;
\u2022 the analysis of segmentation procedures, with a focus on the tissue classification
process, in order to reduce operator dependency and to overcome the
absence of a real gold standard for the evaluation of automatic segmentations;
\u2022 the evaluation and comparison of (unsupervised) segmentation methods, finalized
to define a novel segmentation method for low-field MR images, and for
the local correction/improvement of a given segmentation.
The proposed method is simple but effectively integrates information derived from
medical image analysis and 3D shape analysis. Moreover, the algorithm is general
enough to be applied to different anatomical districts independently of the segmentation
method, imaging techniques (such as CT), or image resolution. The volume
information can be integrated easily in different shape analysis applications, taking
into consideration not only the morphology of the input shape but also the real
context in which it is inserted, to solve clinical tasks. The results obtained by this
combined analysis have been evaluated through statistical analysis
DYNAMIC MEASUREMENT OF THREE-DIMENSIONAL MOTION FROM SINGLE-PERSPECTIVE TWO-DIMENSIONAL RADIOGRAPHIC PROJECTIONS
The digital evolution of the x-ray imaging modality has spurred the development of numerous clinical and research tools. This work focuses on the design, development, and validation of dynamic radiographic imaging and registration techniques to address two distinct medical applications: tracking during image-guided interventions, and the measurement of musculoskeletal joint kinematics.
Fluoroscopy is widely employed to provide intra-procedural image-guidance. However, its planar images provide limited information about the location of surgical tools and targets in three-dimensional space. To address this limitation, registration techniques, which extract three-dimensional tracking and image-guidance information from planar images, were developed and validated in vitro.
The ability to accurately measure joint kinematics in vivo is an important tool in studying both normal joint function and pathologies associated with injury and disease, however it still remains a clinical challenge. A technique to measure joint kinematics from single-perspective x-ray projections was developed and validated in vitro, using clinically available radiography equipmen
A Dynamic-Image Computational Approach for Modeling the Spine
We propose a dynamic-image driven computational approach for the modeling and simulation of the spine. We use static and dynamic medical images, computational methods and anatomic knowledge to accurately model and measure the subject-specific dynamic behavior of structures in the spine. The resulting models have applications in biomechanical simulations, computer animation, and orthopaedic surgery.
We first develop a semi-automated motion reconstruction method for measuring 3D motion with sub-millimeter accuracy. The automation of the method enables the study of subject-specific spine kinematics over large groups of population. The accuracy of the method enables the modeling and analysis of small anatomical features that are difficult to capture in-vivo using existing imaging techniques. We then develop a set of computational tools to model spine soft-tissue structures. We build dynamic-motion driven geometric models that combine the complementary strengths of the accurate but static models used in orthopaedics and the dynamic but low level-of-detail multibody simulations used in humanoid computer animation. Leveraging dynamic images and reconstructed motion, this approach allows the modeling and analysis anatomical features that are too small to be imaged in-vivo and of their dynamic behavior. Finally, we generate predictive, subject-specific models of healthy and symptomatic spines. The predictive models help to identify, understand and validate hypotheses about spine disorders
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A Novel Approach for the Visualisation and Progression Tracking of Metastatic Bone Disease
Metastatic bone disease (MBD) is a common secondary feature of cancer that can cause significant complications, including severe pain and death. Current methods of diagnosis require a highly trained radiologist capable of interpreting medical images and recognising the sites of MBD. These medical images are often noisy, two dimensional, greyscale and usually have a poor resolution.
In order to help assist with these issues, several studies have shown that computer aided methods can locate MBD within medical images. However these methods are limited in scope, accuracy, sensitivity, explainability and do not improve upon the poor visualisations of the underlying medical imaging data.
To address these limitations, I have developed a novel method of automatic MBD assessment and visualisation using computed tomography (CT) imaging data as the input. The method is fully automated and does not require any human interaction -- although users can interact with a viewer that visualises the results. This method has been tested on CT data from prostate cancer patients as prostate cancer is one of the most common sources of MBD.
The method described in this thesis has a sensitivity of 0.871 when detecting sclerotic and lytic lesions within a single data set. This sensitivity is comparable to existing methods, however the scope in detecting these lesions was limited to the vertebrae in previous studies. My method significantly expands this scope to include the ribs, vertebrae, pelvis and proximal femurs.
The work in this thesis also provides novel visualisations of the disease and does not suffer from explainability issues that plague modern machine learning algorithms.
In addition, I developed a novel method of tracking the spread of MBD at multiple time points using longitudinal CT data. This method is capable of calculating the change in lesion volume size across multiple time points, providing a novel numerical assessment.The Armstrong Trus
Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization
In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.).
The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging.
In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place.
We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting
series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf
Shape/image registration for medical imaging : novel algorithms and applications.
This dissertation looks at two different categories of the registration approaches: Shape registration, and Image registration. It also considers the applications of these approaches into the medical imaging field. Shape registration is an important problem in computer vision, computer graphics and medical imaging. It has been handled in different manners in many applications like shapebased segmentation, shape recognition, and tracking. Image registration is the process of overlaying two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors. Many image processing applications like remote sensing, fusion of medical images, and computer-aided surgery need image registration. This study deals with two different applications in the field of medical image analysis. The first one is related to shape-based segmentation of the human vertebral bodies (VBs). The vertebra consists of the VB, spinous, and other anatomical regions. Spinous pedicles, and ribs should not be included in the bone mineral density (BMD) measurements. The VB segmentation is not an easy task since the ribs have similar gray level information. This dissertation investigates two different segmentation approaches. Both of them are obeying the variational shape-based segmentation frameworks. The first approach deals with two dimensional (2D) case. This segmentation approach starts with obtaining the initial segmentation using the intensity/spatial interaction models. Then, shape model is registered to the image domain. Finally, the optimal segmentation is obtained using the optimization of an energy functional which integrating the shape model with the intensity information. The second one is a 3D simultaneous segmentation and registration approach. The information of the intensity is handled by embedding a Willmore flow into the level set segmentation framework. Then the shape variations are estimated using a new distance probabilistic model. The experimental results show that the segmentation accuracy of the framework are much higher than other alternatives. Applications on BMD measurements of vertebral body are given to illustrate the accuracy of the proposed segmentation approach. The second application is related to the field of computer-aided surgery, specifically on ankle fusion surgery. The long-term goal of this work is to apply this technique to ankle fusion surgery to determine the proper size and orientation of the screws that are used for fusing the bones together. In addition, we try to localize the best bone region to fix these screws. To achieve these goals, the 2D-3D registration is introduced. The role of 2D-3D registration is to enhance the quality of the surgical procedure in terms of time and accuracy, and would greatly reduce the need for repeated surgeries; thus, saving the patients time, expense, and trauma
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