34,591 research outputs found
Combined MR brain segmentation
The paper presents a new approach to segmentation of brain from the MR studies. The method is fully automated, very efficient, and quick. The main point of this algorithm is subtraction of T1 series form T2 series (therefore we called it combined), followed by a few image processing steps.The method has been tested using the data sets from three sources. The results were compared numerically to those produced by experts. They indicate great effectiveness of the presented algorithm
Cortical enhanced tissue segmentation of neonatal brain MR images acquired by a dedicated phased array coil
pre-printThe acquisition of high quality MR images of neonatal brains is largely hampered by their characteristically small head size and low tissue contrast. As a result, subsequent image processing and analysis, especially for brain tissue segmentation, are often hindered. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by effectively combing images obtained from 8 coil elements without lengthening data acquisition time. In addition, a subject-specific atlas based tissue segmentation algorithm is specifically developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet-like cortical gray matter (GM) structures in neonatal images with a Hessian filter for generation of cortical GM prior. Then, the prior is combined with our neonatal population atlas to form a cortical enhanced hybrid atlas, which we refer to as the subject-specific atlas. Various experiments are conducted to compare the proposed method with manual segmentation results, as well as with additional two population atlas based segmentation methods. Results show that the proposed method is capable of segmenting the neonatal brain with the highest accuracy, compared to other two methods
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
A cascade of fully convolutional neural networks is proposed to segment
multi-modal Magnetic Resonance (MR) images with brain tumor into background and
three hierarchical regions: whole tumor, tumor core and enhancing tumor core.
The cascade is designed to decompose the multi-class segmentation problem into
a sequence of three binary segmentation problems according to the subregion
hierarchy. The whole tumor is segmented in the first step and the bounding box
of the result is used for the tumor core segmentation in the second step. The
enhancing tumor core is then segmented based on the bounding box of the tumor
core segmentation result. Our networks consist of multiple layers of
anisotropic and dilated convolution filters, and they are combined with
multi-view fusion to reduce false positives. Residual connections and
multi-scale predictions are employed in these networks to boost the
segmentation performance. Experiments with BraTS 2017 validation set show that
the proposed method achieved average Dice scores of 0.7859, 0.9050, 0.8378 for
enhancing tumor core, whole tumor and tumor core, respectively. The
corresponding values for BraTS 2017 testing set were 0.7831, 0.8739, and
0.7748, respectively.Comment: 12 pages, 5 figures. MICCAI Brats Challenge 201
Combining global and local information for the segmentation of MR images of the brain
Magnetic resonance imaging can provide high resolution volumetric images of the brain with exceptional soft tissue contrast. These factors allow the complex structure of the brain to be clearly visualised. This has lead to the development of quantitative methods to analyse neuroanatomical structures. In turn, this has promoted the use of computational methods to automate and improve these techniques. This thesis investigates methods to accurately segment MRI images of the brain. The use of global and local image information is considered, where global information includes image intensity distributions, means and variances and local information is based on the relationship between spatially neighbouring voxels. Methods are explored that aim to improve the classification and segmentation of MR images of the brain by combining these elements. Some common artefacts exist in MR brain images that can be seriously detrimental to image analysis methods. Methods to correct for these artifacts are assessed by exploring their effect, first with some well established classification methods and then with methods that combine global information with local information in the form of a Markov random field model. Another characteristic of MR images is the partial volume effect that occurs where signals from different tissues become mixed over the finite volume of a voxel. This effect is demonstrated and quantified using a simulation. Analysis methods that address these issues are tested on simulated and real MR images. They are also applied to study the structure of the temporal lobes in a group of patients with temporal lobe epilepsy. The results emphasise the benefits and limitations of applying these methods to a problem of this nature. The work in this thesis demonstrates the advantages of using global and local information together in the segmentation of MR brain images and proposes a generalised framework that allows this information to be combined in a flexible way
Low-Rank Based Image Analyses for Pathological MR Image Segmentation and Recovery
The presence of pathologies in magnetic resonance (MR) brain images causes challenges in various image analysis areas, such as registration, atlas construction and atlas-based segmentation. We propose a novel method for the simultaneous recovery and segmentation of pathological MR brain images. Low-rank and sparse decomposition (LSD) approaches have been widely used in this field, decomposing pathological images into (1) low-rank components as recovered images, and (2) sparse components as pathological segmentation. However, conventional LSD approaches often fail to produce recovered images reliably, due to the lack of constraint between low-rank and sparse components. To tackle this problem, we propose a transformed low-rank and structured sparse decomposition (TLS2D) method. The proposed TLS2D integrates the structured sparse constraint, LSD and image alignment into a unified scheme, which is robust for distinguishing pathological regions. Furthermore, the well recovered images can be obtained using TLS2D with the combined structured sparse and computed image saliency as the adaptive sparsity constraint. The efficacy of the proposed method is verified on synthetic and real MR brain tumor images. Experimental results demonstrate that our method can effectively provide satisfactory image recovery and tumor segmentation
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
Improved cerebellar tissue classification on magnetic resonance images of brain.
PURPOSE: To develop and implement a method for improved cerebellar tissue classification on the MRI of brain by automatically isolating the cerebellum prior to segmentation.
MATERIALS AND METHODS: Dual fast spin echo (FSE) and fluid attenuation inversion recovery (FLAIR) images were acquired on 18 normal volunteers on a 3 T Philips scanner. The cerebellum was isolated from the rest of the brain using a symmetric inverse consistent nonlinear registration of individual brain with the parcellated template. The cerebellum was then separated by masking the anatomical image with individual FLAIR images. Tissues in both the cerebellum and rest of the brain were separately classified using hidden Markov random field (HMRF), a parametric method, and then combined to obtain tissue classification of the whole brain. The proposed method for tissue classification on real MR brain images was evaluated subjectively by two experts. The segmentation results on Brainweb images with varying noise and intensity nonuniformity levels were quantitatively compared with the ground truth by computing the Dice similarity indices.
RESULTS: The proposed method significantly improved the cerebellar tissue classification on all normal volunteers included in this study without compromising the classification in remaining part of the brain. The average similarity indices for gray matter (GM) and white matter (WM) in the cerebellum are 89.81 (+/-2.34) and 93.04 (+/-2.41), demonstrating excellent performance of the proposed methodology.
CONCLUSION: The proposed method significantly improved tissue classification in the cerebellum. The GM was overestimated when segmentation was performed on the whole brain as a single object
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Deep learning assisted MRI guided attenuation correction in PET
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPositron emission tomography (PET) is a unique imaging modality that provides physiological
and functional details of the tissue at the molecular level. However, the acquired PET images
have some limitations such as the attenuation. PET attenuation correction is an essential step to
obtain the full potential of PET quantification. With the wide use of hybrid PET/MR scanners,
magnetic resonance (MR) images are used to address the problem of PET attenuation correction.
The MR images segmentation is one simple and robust approach to create pseudo computed
tomography (CT) images, which are used to generate attenuation coefficient maps to correct the
PET attenuation. Recently, deep learning has been proposed and used as a promising technique
to efficiently perform MR and various medical images segmentation.
In this research work, deep learning guided segmentation approaches have been proposed
to enhance the bone class segmentation of MR brain images in order to generate accurate
pseudo-CT images. The first approach has introduced the combination of handcrafted features
with deep learning features to enrich the set of features. Multiresolution analysis techniques,
which generate multiscale and multidirectional coefficients of an image such as contourlet and
shearlet transforms, are applied and combined with deep convolutional neural network (CNN)
features. Different experiments have been conducted to investigate the number of selected
coefficients and the insertion location of the handcrafted features.
The second approach aims at reducing the segmentation algorithm’s complexity while
maintaining the segmentation performance. An attention based convolutional encode-decoder
network has been proposed to adaptively recalibrate the deep network features. This attention based
network consists of two different squeeze and excitation blocks that excite the features
spatially and channel wise. The two blocks are combined sequentially to decrease the number
of network’s parameters and reduces the model complexity. The third approach has been focuses on the application of transfer learning from different MR sequences such as T1 weighted (T1-w) and T2 weighted (T2-w) images. A
pretrained model with T1-w MR sequences is fine tuned to perform the segmentation of T2-w
images. Multiple fine tuning approaches and experiments have been conducted to study the best
fine tuning mechanism that is able to build an efficient segmentation model for both T1-w and
T2-w segmentation. Clinical datasets of fifty patients with different conditions and diagnosis have been
used to carry an objective evaluation to measure the segmentation performance of the results
obtained by the three proposed methods. The first and second approaches have been validated
with other studies in the literature that applied deep network based segmentation technique to
perform MR based attenuation correction for PET images. The proposed methods have shown
an enhancement in the bone segmentation with an increase of dice similarity coefficient (DSC)
from 0.6179 to 0.6567 using an ensemble of CNNs with an improvement percentage of 6.3%.
The proposed excitation-based CNN has decreased the model complexity by decreasing the
number of trainable parameters by more than 46% where less computing resources are required
to train the model. The proposed hybrid transfer learning method has shown its superiority to
build a multi-sequences (T1-w and T2-w) segmentation approach compared to other applied
transfer learning methods especially with the bone class where the DSC is increased from 0.3841
to 0.5393. Moreover, the hybrid transfer learning approach requires less computing time than
transfer learning using open and conservative fine tuning
Challenges in Brain Magnetic Resonance Image Segmentation
Over the past several decades, the application of magnetic resonance imaging (MRI) has been rapidly expanding in the areas of brain research studies and clinical diagnosis. One of the most important steps in brain MRI data preparation is the removal of unwanted brain regions, which is followed by segmentation of the brain into three main regions – white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) – or into subregions. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain’s anatomical structures, analyzing brain changes, delineating pathological regions, and surgical planning and image-guided interventions. Brain segmentation allows clinicians and researchers to concentrate on a specific region in the brain in their analyses. However, brain segmentation is a difficult task due to high similarities and correlations of image intensity among brain regions. In this review, image segmentation algorithms used for dividing the brain into different sectors were discussed in detail. The potential for using the fuzzy c-means (FCM) unsupervised clustering algorithm and certain hybrid (combined) methods to segment brain MR images was demonstrated. Additionally, certain validation techniques that are required to demonstrate the performance of segmentation methods in terms of accuracy rates were described.
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