180 research outputs found
Probabilistic partial volume modelling of biomedical tomographic image data
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
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
Algorithmic Analysis Techniques for Molecular Imaging
This study addresses image processing techniques for two medical imaging
modalities: Positron Emission Tomography (PET) and Magnetic Resonance
Imaging (MRI), which can be used in studies of human body functions and
anatomy in a non-invasive manner.
In PET, the so-called Partial Volume Effect (PVE) is caused by low
spatial resolution of the modality. The efficiency of a set of PVE-correction
methods is evaluated in the present study. These methods use information
about tissue borders which have been acquired with the MRI technique. As
another technique, a novel method is proposed for MRI brain image segmen-
tation. A standard way of brain MRI is to use spatial prior information
in image segmentation. While this works for adults and healthy neonates,
the large variations in premature infants preclude its direct application.
The proposed technique can be applied to both healthy and non-healthy
premature infant brain MR images. Diffusion Weighted Imaging (DWI) is
a MRI-based technique that can be used to create images for measuring
physiological properties of cells on the structural level. We optimise the
scanning parameters of DWI so that the required acquisition time can be
reduced while still maintaining good image quality.
In the present work, PVE correction methods, and physiological DWI
models are evaluated in terms of repeatabilityof the results. This gives in-
formation on the reliability of the measures given by the methods. The
evaluations are done using physical phantom objects, correlation measure-
ments against expert segmentations, computer simulations with realistic
noise modelling, and with repeated measurements conducted on real pa-
tients. In PET, the applicability and selection of a suitable partial volume
correction method was found to depend on the target application. For MRI,
the data-driven segmentation offers an alternative when using spatial prior is
not feasible. For DWI, the distribution of b-values turns out to be a central
factor affecting the time-quality ratio of the DWI acquisition. An optimal
b-value distribution was determined. This helps to shorten the imaging time
without hampering the diagnostic accuracy.Siirretty Doriast
Adaptive kernel estimation for enhanced filtering and pattern classification of magnetic resonance imaging: novel techniques for evaluating the biomechanics and pathologic conditions of the lumbar spine
This dissertation investigates the contribution the lumbar spine musculature has on etiological and pathogenic characteristics of low back pain and lumbar spondylosis. This endeavor necessarily required a two-step process: 1) design of an accurate post-processing method for extracting relevant information via magnetic resonance images and 2) determine pathological trends by elucidating high-dimensional datasets through multivariate pattern classification. The lumbar musculature was initially evaluated by post-processing and segmentation of magnetic resonance (MR) images of the lumbar spine, which characteristically suffer from nonlinear corruption of the signal intensity. This so called intensity inhomogeneity degrades the efficacy of traditional intensity-based segmentation algorithms. Proposed in this dissertation is a solution for filtering individual MR images by extracting a map of the underlying intensity inhomogeneity to adaptively generate local estimates of the kernel’s optimal bandwidth. The adaptive kernel is implemented and tested within the structure of the non-local means filter, but also generalized and extended to the Gaussian and anisotropic diffusion filters. Testing of the proposed filters showed that the adaptive kernel significantly outperformed their non-adaptive counterparts. A variety of performance metrics were utilized to measure either fine feature preservation or accuracy of post-processed segmentation. Based on these metrics the adaptive filters proposed in this dissertation significantly outperformed the non-adaptive versions. Using the proposed filter, the MR data was semi-automatically segmented to delineate between adipose and lean muscle tissues. Two important findings were reached utilizing this data. First, a clear distinction between the musculature of males and females was established that provided 100% accuracy in being able to predict gender. Second, degenerative lumbar spines were accurately predicted at a rate of up to 92% accuracy. These results solidify prior assumptions made regarding sexual dimorphic anatomy and the pathogenic nature of degenerative spine disease
On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis
Magnetic Resonance Imaging (MRI) is considered the gold standard of medical
imaging because of the excellent soft-tissue contrast exhibited in the images
reconstructed by the MRI pipeline, which in-turn enables the human radiologist
to discern many pathologies easily. More recently, Deep Learning (DL) models
have also achieved state-of-the-art performance in diagnosing multiple diseases
using these reconstructed images as input. However, the image reconstruction
process within the MRI pipeline, which requires the use of complex hardware and
adjustment of a large number of scanner parameters, is highly susceptible to
noise of various forms, resulting in arbitrary artifacts within the images.
Furthermore, the noise distribution is not stationary and varies within a
machine, across machines, and patients, leading to varying artifacts within the
images. Unfortunately, DL models are quite sensitive to these varying artifacts
as it leads to changes in the input data distribution between the training and
testing phases. The lack of robustness of these models against varying
artifacts impedes their use in medical applications where safety is critical.
In this work, we focus on improving the generalization performance of these
models in the presence of multiple varying artifacts that manifest due to the
complexity of the MR data acquisition. In our experiments, we observe that
Batch Normalization, a widely used technique during the training of DL models
for medical image analysis, is a significant cause of performance degradation
in these changing environments. As a solution, we propose to use other
normalization techniques, such as Group Normalization and Layer Normalization
(LN), to inject robustness into model performance against varying image
artifacts. Through a systematic set of experiments, we show that GN and LN
provide better accuracy for various MR artifacts and distribution shifts.Comment: Accepted at MIDL 202
On motion in dynamic magnetic resonance imaging: Applications in cardiac function and abdominal diffusion
La imagen por resonancia magnética (MRI), hoy en dÃa, representa una potente herramienta para el diagnóstico clÃnico debido a su flexibilidad y sensibilidad a un amplio rango de propiedades del tejido. Sus principales ventajas son su sobresaliente versatilidad y su capacidad para proporcionar alto contraste entre tejidos blandos. Gracias a esa versatilidad, la MRI se puede emplear para observar diferentes fenómenos fÃsicos dentro del cuerpo humano combinando distintos tipos de pulsos dentro de la secuencia. Esto ha permitido crear distintas modalidades con múltiples aplicaciones tanto biológicas como clÃnicas. La adquisición de MR es, sin embargo, un proceso lento, lo que conlleva una solución de compromiso entre resolución y tiempo de adquisición (Lima da Cruz, 2016; Royuela-del Val, 2017). Debido a esto, la presencia de movimiento fisiológico durante la adquisición puede conllevar una grave degradación de la calidad de imagen, asà como un incremento del tiempo de adquisición, aumentando asà tambien la incomodidad del paciente. Esta limitación práctica representa un gran obstáculo para la viabilidad clÃnica de la MRI. En esta Tesis Doctoral se abordan dos problemas de interés en el campo de la MRI en los que el movimiento fisiológico tiene un papel protagonista. Éstos son, por un lado, la estimación robusta de parámetros de rotación y esfuerzo miocárdico a partir de imágenes de MR-Tagging dinámica para el diagnóstico y clasificación de cardiomiopatÃas y, por otro, la reconstrucción de mapas del coeficiente de difusión aparente (ADC) a alta resolución y con alta relación señal a ruido (SNR) a partir de adquisiciones de imagen ponderada en difusión (DWI) multiparamétrica en el hÃgado.Departamento de TeorÃa de la Señal y Comunicaciones e IngenierÃa TelemáticaDoctorado en TecnologÃas de la Información y las Telecomunicacione
Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images
Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE) cardiovascular magnetic
resonance (CMR) imaging for guiding the management of patients with sequelae to myocardial infarction,
such as ventricular tachycardia and heart failure. Clinical implementation of these developments necessitates
a reproducible and reliable segmentation of the infarcted regions. It is challenging to compare
new algorithms for infarct segmentation in the left ventricle (LV) with existing algorithms. Benchmarking
datasets with evaluation strategies are much needed to facilitate comparison. This manuscript presents
a benchmarking evaluation framework for future algorithms that segment infarct from LGE CMR of the
LV. The image database consists of 30 LGE CMR images of both humans and pigs that were acquired
from two separate imaging centres. A consensus ground truth was obtained for all data using maximum
likelihood estimation.
Six widely-used fixed-thresholding methods and five recently developed algorithms are tested on the
benchmarking framework. Results demonstrate that the algorithms have better overlap with the consensus
ground truth than most of the n-SD fixed-thresholding methods, with the exception of the FullWidth-at-Half-Maximum
(FWHM) fixed-thresholding method. Some of the pitfalls of fixed thresholding
methods are demonstrated in this work. The benchmarking evaluation framework, which is a contribution
of this work, can be used to test and benchmark future algorithms that detect and quantify infarct
in LGE CMR images of the LV. The datasets, ground truth and evaluation code have been made publicly
available through the website: https://www.cardiacatlas.org/web/guest/challenges
Myocardial Infarction Quantification From Late Gadolinium Enhancement MRI Using Top-hat Transforms and Neural Networks
Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-MRI)
is the gold standard technique for myocardial viability assessment. Although
the technique accurately reflects the damaged tissue, there is no clinical
standard for quantifying myocardial infarction (MI), demanding most algorithms
to be expert dependent. Objectives and Methods: In this work a new automatic
method for MI quantification from LGE-MRI is proposed. Our novel segmentation
approach is devised for accurately detecting not only hyper-enhanced lesions,
but also microvascular-obstructed areas. Moreover, it includes a myocardial
disease detection step which extends the algorithm for working under healthy
scans. The method is based on a cascade approach where firstly, diseased slices
are identified by a convolutional neural network (CNN). Secondly, by means of
morphological operations a fast coarse scar segmentation is obtained. Thirdly,
the segmentation is refined by a boundary-voxel reclassification strategy using
an ensemble of CNNs. For its validation, reproducibility and further comparison
against other methods, we tested the method on a big multi-field expert
annotated LGE-MRI database including healthy and diseased cases. Results and
Conclusion: In an exhaustive comparison against nine reference algorithms, the
proposal achieved state-of-the-art segmentation performances and showed to be
the only method agreeing in volumetric scar quantification with the expert
delineations. Moreover, the method was able to reproduce the intra- and
inter-observer variability ranges. It is concluded that the method could
suitably be transferred to clinical scenarios.Comment: Submitted to IEE
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