138 research outputs found
Automated measurement of fat infiltration in the hip abductors from Dixon magnetic resonance imaging
PURPOSE: Intramuscular fat infiltration is a dynamic process, in response to exercise and muscle health, which can be quantified by estimating fat fraction (FF) from Dixon MRI. Healthy hip abductor muscles are a good indicator of a healthy hip and an active lifestyle as they have a fundamental role in walking. The automated measurement of the abductors' FF requires the challenging task of segmenting them. We aimed to design, develop and evaluate a multi-atlas based method for automated measurement of fat fraction in the main hip abductor muscles: gluteus maximus (GMAX), gluteus medius (GMED), gluteus minimus (GMIN) and tensor fasciae latae (TFL). METHOD: We collected and manually segmented Dixon MR images of 10 healthy individuals and 7 patients who underwent MRI for hip problems. Twelve of them were selected to build an atlas library used to implement the automated multi-atlas segmentation method. We compared the FF in the hip abductor muscles for the automated and manual segmentations for both healthy and patients groups. Measures of average and spread were reported for FF for both methods. We used the root mean square error (RMSE) to quantify the method accuracy. A linear regression model was used to explain the relationship between FF for automated and manual segmentations. RESULTS: The automated median (IQR) FF was 20.0(16.0-26.4) %, 14.3(10.9-16.5) %, 15.5(13.9-18.6) % and 16.2(13.5-25.6) % for GMAX, GMED, GMIN and TFL respectively, with a FF RMSE of 1.6%, 0.8%, 2.1%, 2.7%. A strong linear correlation (R2 = 0.93, p < .001, m = 0.99) was found between the FF from automated and manual segmentations. The mean FF was higher in patients than in healthy subjects. CONCLUSION: The automated measurement of FF of hip abductor muscles from Dixon MRI had good agreement with FF measurements from manually segmented images. The method was accurate for both healthy and patients groups
Automating the multimodal analysis of musculoskeletal imaging in the presence of hip implants
In patients treated with hip arthroplasty, the muscular condition and presence of inflammatory reactions are assessed using magnetic resonance imaging (MRI). As MRI lacks contrast for bony structures, computed tomography (CT) is preferred for clinical evaluation of bone tissue and orthopaedic surgical planning. Combining the complementary information of MRI and CT could improve current clinical practice for diagnosis, monitoring and treatment planning. In particular, the different contrast of these modalities could help better quantify the presence of fatty infiltration to characterise muscular condition after hip replacement. In this thesis, I developed automated processing tools for the joint analysis of CT and MR images of patients with hip implants. In order to combine the multimodal information, a novel nonlinear registration algorithm was introduced, which imposes rigidity constraints on bony structures to ensure realistic deformation. I implemented and thoroughly validated a fully automated framework for the multimodal segmentation of healthy and pathological musculoskeletal structures, as well as implants. This framework combines the proposed registration algorithm with tailored image quality enhancement techniques and a multi-atlas-based segmentation approach, providing robustness against the large population anatomical variability and the presence of noise and artefacts in the images. The automation of muscle segmentation enabled the derivation of a measure of fatty infiltration, the Intramuscular Fat Fraction, useful to characterise the presence of muscle atrophy. The proposed imaging biomarker was shown to strongly correlate with the atrophy radiological score currently used in clinical practice. Finally, a preliminary work on multimodal metal artefact reduction, using an unsupervised deep learning strategy, showed promise for improving the postprocessing of CT and MR images heavily corrupted by metal artefact. This work represents a step forward towards the automation of image analysis in hip arthroplasty, supporting and quantitatively informing the decision-making process about patient’s management
Intramuscular fat in gluteus maximus for different levels of physical activity
We aimed to determine if gluteus maximus (GMAX) fat infiltration is associated with different levels of physical activity. Identifying and quantifying differences in the intramuscular fat content of GMAX in subjects with different levels of physical activity can provide a new tool to evaluate hip muscles health. This was a cross-sectional study involving seventy subjects that underwent Dixon MRI of the pelvis. The individuals were divided into four groups by levels of physical activity, from low to high: inactive patients due to hip pain; and low, medium and high physical activity groups of healthy subjects (HS) based on hours of exercise per week. We estimated the GMAX intramuscular fat content for each subject using automated measurements of fat fraction (FF) from Dixon images. The GMAX volume and lean volume were also measured and normalized by lean body mass. The effects of body mass index (BMI) and age were included in the statistical analysis. The patient group had a significantly higher FF than the three groups of HS (median values of 26.2%, 17.8%, 16.7% and 13.7% respectively, p < 0.001). The normalized lean volume was significantly larger in the high activity group compared to all the other groups (p < 0.001, p = 0.002 and p = 0.02). Employing a hierarchical linear regression analysis, we found that hip pain, low physical activity, female gender and high BMI were statistically significant predictors of increased GMAX fat infiltration
Muscle volume quantification: guiding transformers with anatomical priors
Muscle volume is a useful quantitative biomarker in sports, but also for the
follow-up of degenerative musculo-skelletal diseases. In addition to volume,
other shape biomarkers can be extracted by segmenting the muscles of interest
from medical images. Manual segmentation is still today the gold standard for
such measurements despite being very time-consuming. We propose a method for
automatic segmentation of 18 muscles of the lower limb on 3D Magnetic Resonance
Images to assist such morphometric analysis. By their nature, the tissue of
different muscles is undistinguishable when observed in MR Images. Thus, muscle
segmentation algorithms cannot rely on appearance but only on contour cues.
However, such contours are hard to detect and their thickness varies across
subjects. To cope with the above challenges, we propose a segmentation approach
based on a hybrid architecture, combining convolutional and visual transformer
blocks. We investigate for the first time the behaviour of such hybrid
architectures in the context of muscle segmentation for shape analysis.
Considering the consistent anatomical muscle configuration, we rely on
transformer blocks to capture the longrange relations between the muscles. To
further exploit the anatomical priors, a second contribution of this work
consists in adding a regularisation loss based on an adjacency matrix of
plausible muscle neighbourhoods estimated from the training data. Our
experimental results on a unique database of elite athletes show it is possible
to train complex hybrid models from a relatively small database of large
volumes, while the anatomical prior regularisation favours better predictions
A Geometric Flow Approach for Segmentation of Images with Inhomongeneous Intensity and Missing Boundaries
Image segmentation is a complex mathematical problem, especially for images
that contain intensity inhomogeneity and tightly packed objects with missing
boundaries in between. For instance, Magnetic Resonance (MR) muscle images
often contain both of these issues, making muscle segmentation especially
difficult. In this paper we propose a novel intensity correction and a
semi-automatic active contour based segmentation approach. The approach uses a
geometric flow that incorporates a reproducing kernel Hilbert space (RKHS) edge
detector and a geodesic distance penalty term from a set of markers and
anti-markers. We test the proposed scheme on MR muscle segmentation and compare
with some state of the art methods. To help deal with the intensity
inhomogeneity in this particular kind of image, a new approach to estimate the
bias field using a fat fraction image, called Prior Bias-Corrected Fuzzy
C-means (PBCFCM), is introduced. Numerical experiments show that the proposed
scheme leads to significantly better results than compared ones. The average
dice values of the proposed method are 92.5%, 85.3%, 85.3% for quadriceps,
hamstrings and other muscle groups while other approaches are at least 10%
worse.Comment: Presented at CVIT 2023 Conference. Accepted to Journal of Image and
Graphic
Joint Multimodal Segmentation of Clinical CT and MR from Hip Arthroplasty Patients
Magnetic resonance imaging (MRI) is routinely employed to assess muscular response and presence of inflammatory reactions in patients treated with metal-on-metal hip arthroplasty, driving the decision for revision surgery. However, MRI is lacking contrast for bony structures and as a result orthopaedic surgical planning is mostly performed on computed tomography images. In this paper, we combine the complementary information of both modalities into a novel framework for the joint segmentation of healthy and pathological musculoskeletal structures as well as implants on all images. Our processing pipeline is fully automated and was designed to handle the highly anisotropic resolution of clinical MR images by means of super resolution reconstruction. The accuracy of the intra-subject multimodal registration was improved by employing a non-linear registration algorithm with hard constraints on the deformation of bony structures, while a multi-atlas segmentation propagation approach provided robustness to the large shape variability in the population. The suggested framework was evaluated in a leave-one-out cross-validation study on 20 hip sides. The proposed pipeline has potential for the extraction of clinically relevant imaging biomarkers for implant failure detection
Registration and Deformable Model-Based Neck Muscles Segmentation and 3D Reconstruction
Whiplash is a very common ailment encountered in clinical practice that is usually a result of vehicle accidents but also domestic activities and sports injuries. It is normally caused when neck organs (specifically muscles) are impaired. Whiplash-associated disorders include acute headaches, neck pain, stiffness, arm dislocation, abnormal sensations, and auditory and optic problems, the persistence of which may be chronic or acute. Insurance companies compensate almost fifty percent of claims lodged due to whiplash injury through compulsory third party motor insurance. The morphological structures of neck muscles undergo hypertrophy or atrophy following damage caused to them by accidents. Before any medical treatment is applied , any such change needs to be known which requires 3D visualization of the neck muscles through a proper
segmentation of them because the neck contains many other sensitive organs such as nerves, blood vessels, the spinal cord and trachea.
The segmentation of neck muscles in medical images is a more challenging task than those of other muscles and organs due to their similar densities and compactness, low resolutions and contrast in medical images, anatomical variabilities among individuals, noise, inhomogeneity of medical images and false boundaries created by intra-muscular fat. Traditional segmentation algorithms, such as those used in thresholding and clustering-based methods, are not applicable in this project and also not suitable for medical images. Although there are some techniques available in clinical research for segmenting muscles such as thigh, tongue, leg, hip and pectoral ones, to the best of author's knowledge, there are no methods available for segmenting neck muscles due to the challenges described above.
In the first part of this dissertation, an atlas-based method for segmenting MR images, which uses linear and non-linear registration frameworks, is proposed, with output from the registration process further refined by a novel parametric deformable model. The proposed method is tested on real clinical data of both healthy and non-healthy individuals. During the last few decades, registration- and deformable model-based segmentation methods have been very popular for medical image segmentation due to their incorporation of prior information. While registration-based segmentation techniques can preserve topologies of objects in an image, accuracy of atlas-based segmentation depends mainly on an effective registration process. In this study, the registration framework is designed in a novel way in which images are initially registered by a distinct 3D affine transformation and then aligned by a local elastic geometrical transformation based on discrete cosines and registered firstly slice-wise and then block-wise. The numbers of motion parameters are changed in three different steps per frame. This proposed registration framework can handle anatomical variabilities and pathologies by confining its parameters in local regions. Also, as warping of the framework relies on number of motion parameters, similarities between two images, gradients of floating image and coordinate mesh grid values, it can easily manage pathological and anatomical variabilities using a hierarchical parameter scheme.
The labels transferred from atlas can be improved by deformable model-based segmentation. Although geometric deformable models have been widely used in many biomedical applications over recent years, they cannot work in the context of neck muscles segmentation due to noise, background clutter and similar objects touching each other. Another important drawback of geometric deformable models is that they are many
times slower than parametric deformable ones. Therefore, the segmentation results produced by the registration process are ameliorated using a multiple-object parametric deformable model which is discussed in detail in the second part of this thesis. This algorithm uses a novel Gaussian potential energy distribution which can adapt to topological changes and does not require re-parameterization. Also, it incorporates a new overlap removal technique which ensures that there are no overlaps or gaps inside an object. Furthermore, stopping criteria of vertices are designed so that difference between boundaries of the deformable model and actual object is minimal.
The multiple-object parametric deformable model is also applied in a template contours propagation-based segmentation technique, as discussed in the third part of this dissertation. This method is semi-automatic, whereby a manual delineation of middle image in a MRI data set is required. It can handle anatomical variabilities more easily than atlas-based segmentation because it can segment any individual's data irrespective of his/her age, weight and height with low computational complexity and it does not depend on other data as it operates semi-automatically. In it, initial model contour resides close to the object's boundary, with degree of closeness dependent on slice thicknesses and gaps between the slices
Semi-automatic segmentation and surface reconstruction of computed tomography images by using rotoscoping and warping techniques
Background: Quick and large-scale segmentation along with three-dimensional (3D) reconstruction is necessary to make precise 3D musculoskeletal models for surface anatomy education, palpation training, medical communication, morphology research, and virtual surgery simulation. However, automatic segmentation of the skin and muscles remain undeveloped.
Materials and methods: Therefore, in this study, we developed workflows for semi-automatic segmentation and surface reconstruction, using rotoscoping and warping techniques.
Results: The techniques were applied to multi detector computed tomography images, which were optimised to quickly generate surface models of the skin and the anatomical structures underlying the fat tissue.
Conclusions: The workflows developed in this study are expected to enable researchers to create segmented images and optimised surface models from any set of serially sectioned images quickly and conveniently. Moreover, these optimised surface models can easily be modified for further application or educational use
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