8,373 research outputs found
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
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Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.
PurposeTo assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry.MethodsWe trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics.ResultsDice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]).ConclusionsUtilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization
PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation
With the advent of convolutional neural networks~(CNN), supervised learning
methods are increasingly being used for whole brain segmentation. However, a
large, manually annotated training dataset of labeled brain images required to
train such supervised methods is frequently difficult to obtain or create. In
addition, existing training datasets are generally acquired with a homogeneous
magnetic resonance imaging~(MRI) acquisition protocol. CNNs trained on such
datasets are unable to generalize on test data with different acquisition
protocols. Modern neuroimaging studies and clinical trials are necessarily
multi-center initiatives with a wide variety of acquisition protocols. Despite
stringent protocol harmonization practices, it is very difficult to standardize
the gamut of MRI imaging parameters across scanners, field strengths, receive
coils etc., that affect image contrast. In this paper we propose a CNN-based
segmentation algorithm that, in addition to being highly accurate and fast, is
also resilient to variation in the input acquisition. Our approach relies on
building approximate forward models of pulse sequences that produce a typical
test image. For a given pulse sequence, we use its forward model to generate
plausible, synthetic training examples that appear as if they were acquired in
a scanner with that pulse sequence. Sampling over a wide variety of pulse
sequences results in a wide variety of augmented training examples that help
build an image contrast invariant model. Our method trains a single CNN that
can segment input MRI images with acquisition parameters as disparate as
-weighted and -weighted contrasts with only -weighted training
data. The segmentations generated are highly accurate with state-of-the-art
results~(overall Dice overlap), with a fast run time~( 45
seconds), and consistent across a wide range of acquisition protocols.Comment: Typo in author name corrected. Greves -> Grev
Segmentation of brain MRI during early childhood
The objective of this thesis is the development of automatic methods to measure the changes in
volume and growth of brain structures in prematurely born infants. Automatic tools for accurate
tissue quantification from magnetic resonance images can provide means for understanding
how the neurodevelopmental effects of the premature birth, such as cognitive, neurological or
behavioural impairment, are related to underlying changes in brain anatomy. Understanding
these changes forms a basis for development of suitable treatments to improve the outcomes of
premature birth.
In this thesis we focus on the segmentation of brain structures from magnetic resonance images
during early childhood. Most of the current brain segmentation techniques have been focused
on the segmentation of adult or neonatal brains. As a result of rapid development, the brain
anatomy during early childhood differs from anatomy of both adult and neonatal brains and
therefore requires adaptations of available techniques to produce good results.
To address the issue of anatomical differences of the brain during early childhood compared
to other age-groups, population-specific deformable and probabilistic atlases are introduced. A
method for generation of population-specific prior information in form of a probabilistic atlas
is proposed and used to enhance existing segmentation algorithms.
The evaluation of registration-based and intensity-based approaches shows the techniques to
be complementary in the quality of automatic segmentation in different parts of the brain. We
propose a novel robust segmentation method combining the advantages of both approaches. The
method is based on multiple label propagation using B-spline non-rigid registration followed by
EM segmentation.
Intensity inhomogeneity is a shading artefact resulting from the acquisition process, which
significantly affects modern high resolution MR data acquired at higher magnetic field strengths.
A novel template based method focused on correcting the intensity inhomogeneity in data
acquired at higher magnetic field strengths is therefore proposed.
The proposed segmentation method combined with proposed intensity inhomogeneity correction
method offers a robust tool for quantification of volumes and growth of brain structures during
early childhood. The tool have been applied to 67 T1-weigted images of subject at one and two years of age
Ten simple rules for reporting voxel-based morphometry studies
Voxel-based morphometry [Ashburner, J. and Friston, K.J., 2000. Voxel-based morphometry—the methods. NeuroImage 11(6 Pt 1), 805–821] is a commonly used tool for studying patterns of brain change in development or disease and neuroanatomical correlates of subject characteristics. In performing a VBM study, many methodological options are available; if the study is to be easily interpretable and repeatable, the processing steps and decisions must be clearly described. Similarly, unusual methods and parameter choices should be justified in order to aid readers in judging the importance of such options or in comparing the work with other studies. This editorial suggests core principles that should be followed and information that should be included when reporting a VBM study in order to make it transparent, replicable and useful
Keypoint Transfer for Fast Whole-Body Segmentation
We introduce an approach for image segmentation based on sparse
correspondences between keypoints in testing and training images. Keypoints
represent automatically identified distinctive image locations, where each
keypoint correspondence suggests a transformation between images. We use these
correspondences to transfer label maps of entire organs from the training
images to the test image. The keypoint transfer algorithm includes three steps:
(i) keypoint matching, (ii) voting-based keypoint labeling, and (iii)
keypoint-based probabilistic transfer of organ segmentations. We report
segmentation results for abdominal organs in whole-body CT and MRI, as well as
in contrast-enhanced CT and MRI. Our method offers a speed-up of about three
orders of magnitude in comparison to common multi-atlas segmentation, while
achieving an accuracy that compares favorably. Moreover, keypoint transfer does
not require the registration to an atlas or a training phase. Finally, the
method allows for the segmentation of scans with highly variable field-of-view.Comment: Accepted for publication at IEEE Transactions on Medical Imagin
Fast and Robust Automatic Segmentation Methods for MR Images of Injured and Cancerous Tissues
Magnetic Resonance Imaging: MRI) is a key medical imaging technology. Through in vivo soft tissue imaging, MRI allows clinicians and researchers to make diagnoses and evaluations that were previously possible only through biopsy or autopsy. However, analysis of MR images by domain experts can be time-consuming, complex, and subject to bias. The development of automatic segmentation techniques that make use of robust statistical methods allows for fast and unbiased analysis of MR images.
In this dissertation, I propose segmentation methods that fall into two classes---(a) segmentation via optimization of a parametric boundary, and: b) segmentation via multistep, spatially constrained intensity classification. These two approaches are applicable in different segmentation scenarios. Parametric boundary segmentation is useful and necessary for segmentation of noisy images where the tissue of interest has predictable shape but poor boundary delineation, as in the case of lung with heavy or diffuse tumor. Spatially constrained intensity classification is appropriate for segmentation of noisy images with moderate contrast between tissue regions, where the areas of interest have unpredictable shapes, as is the case in spinal injury and brain tumor. The proposed automated segmentation techniques address the need for MR image analysis in three specific applications:: 1) preclinical rodent studies of primary and metastatic lung cancer: approach: a)),: 2) preclinical rodent studies of spinal cord lesion: approach: b)), and: 3) postclinical analysis of human brain cancer: approach: b)).
In preclinical rodent studies of primary and metastatic lung cancer, respiratory-gated MRI is used to quantitatively measure lung-tumor burden and monitor the time-course progression of individual tumors. I validate a method for measuring tumor burden based upon average lung-image intensity. The method requires accurate lung segmentation; toward this end, I propose an automated lung segmentation method that works for varying tumor burden levels. The method includes development of a novel, two-dimensional parametric model of the mouse lungs and a multifaceted cost function to optimally fit the model parameters to each image. Results demonstrate a strong correlation: 0.93), comparable with that of fully manual expert segmentation, between the automated method\u27s tumor-burden metric and the tumor burden measured by lung weight.
In preclinical rodent studies of spinal cord lesion, MRI is used to quantify tissues in control and injured mouse spinal cords. For this application, I propose a novel, multistep, multidimensional approach, utilizing the Classification Expectation Maximization: CEM) algorithm, for automatic segmentation of spinal cord tissues. In contrast to previous methods, my proposed method incorporates prior knowledge of cord geometry and the distinct information contained in the different MR images gathered. Unlike previous approaches, the algorithm is shown to remain accurate for whole spinal cord, white matter, and hemorrhage segmentation, even in the presence of significant injury. The results of the method are shown to be on par with expert manual segmentation.
In postclinical analysis of human brain cancer, access to large collections of MRI data enables scientifically rigorous study of cancers like glioblastoma multiforme, the most common form of malignant primary brain tumor. For this application, I propose an efficient and effective automated segmentation method, the Enhanced Classification Expectation Maximization: ECEM) algorithm. The ECEM algorithm is novel in that it introduces spatial information directly into the classical CEM algorithm, which is otherwise spatially unaware, with low additional computational complexity. I compare the ECEM\u27s performance on simulated data to the standard finite Gaussian mixture EM algorithm, which is not spatially aware, and to the hidden-Markov random field EM algorithm, a commonly-used spatially aware automated segmentation method for MR brain images. I also show sample results demonstrating the ECEM algorithm\u27s ability to segment MR images of glioblastoma
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