313 research outputs found
Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies
Multi-atlas segmentation is a widely used tool in medical image analysis,
providing robust and accurate results by learning from annotated atlas
datasets. However, the availability of fully annotated atlas images for
training is limited due to the time required for the labelling task.
Segmentation methods requiring only a proportion of each atlas image to be
labelled could therefore reduce the workload on expert raters tasked with
annotating atlas images. To address this issue, we first re-examine the
labelling problem common in many existing approaches and formulate its solution
in terms of a Markov Random Field energy minimisation problem on a graph
connecting atlases and the target image. This provides a unifying framework for
multi-atlas segmentation. We then show how modifications in the graph
configuration of the proposed framework enable the use of partially annotated
atlas images and investigate different partial annotation strategies. The
proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets
for hippocampal and cardiac segmentation. Experiments were performed aimed at
(1) recreating existing segmentation techniques with the proposed framework and
(2) demonstrating the potential of employing sparsely annotated atlas data for
multi-atlas segmentation
Automated Extraction of Biomarkers for Alzheimer's Disease from Brain Magnetic Resonance Images
In this work, different techniques for the automated extraction of biomarkers for
Alzheimer's disease (AD) from brain magnetic resonance imaging (MRI) are proposed.
The described work forms part of PredictAD (www.predictad.eu), a joined
European research project aiming at the identification of a unified biomarker for AD
combining different clinical and imaging measurements. Two different approaches are
followed in this thesis towards the extraction of MRI-based biomarkers: (I) the extraction
of traditional morphological biomarkers based on neuronatomical structures
and (II) the extraction of data-driven biomarkers applying machine-learning techniques.
A novel method for a unified and automated estimation of structural volumes
and volume changes is proposed. Furthermore, a new technique that allows the low-dimensional
representation of a high-dimensional image population for data analysis
and visualization is described. All presented methods are evaluated on images from
the Alzheimer's Disease Neuroimaging Initiative (ADNI), providing a large and diverse
clinical database. A rigorous evaluation of the power of all identified biomarkers to
discriminate between clinical subject groups is presented. In addition, the agreement
of automatically derived volumes with reference labels as well as the power of the
proposed method to measure changes in a subject's atrophy rate are assessed. The
proposed methods compare favorably to state-of-the art techniques in neuroimaging
in terms of accuracy, robustness and run-time
Patch-based segmentation with spatial context for medical image analysis
Accurate segmentations in medical imaging form a crucial role in many applications from pa-
tient diagnosis to population studies. As the amount of data generated from medical images
increases, the ability to perform this task without human intervention becomes ever more de-
sirable. One approach, known broadly as atlas-based segmentation, is to propagate labels from
images which have already been manually labelled by clinical experts. Methods using this ap-
proach have been shown to be e ective in many applications, demonstrating great potential for
automatic labelling of large datasets. However, these methods usually require the use of image
registration and are dependent on the outcome of the registration. Any registrations errors
that occur are also propagated to the segmentation process and are likely to have an adverse
e ect on segmentation accuracy. Recently, patch-based methods have been shown to allow a
relaxation of the required image alignment, whilst achieving similar results. In general, these
methods label each voxel of a target image by comparing the image patch centred on the voxel
with neighbouring patches from an atlas library and assigning the most likely label according
to the closest matches. The main contributions of this thesis focuses around this approach
in providing accurate segmentation results whilst minimising the dependency on registration
quality. In particular, this thesis proposes a novel kNN patch-based segmentation framework,
which utilises both intensity and spatial information, and explore the use of spatial context in
a diverse range of applications. The proposed methods extend the potential for patch-based
segmentation to tolerate registration errors by rede ning the \locality" for patch selection and
comparison, whilst also allowing similar looking patches from di erent anatomical structures
to be di erentiated. The methods are evaluated on a wide variety of image datasets, ranging
from the brain to the knees, demonstrating its potential with results which are competitive to
state-of-the-art techniques.Open Acces
Concatenated spatially-localized random forests for hippocampus labeling in adult and infant MR brain images
Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to the ambiguous intensity boundary between the hippocampus and surrounding anatomies. In this paper, we propose a concatenated set of spatially-localized random forests for multi-atlas-based hippocampus labeling of adult/infant brain MR images. The contribution in our work is two-fold. First, each forest classifier is trained to label just a specific sub-region of the hippocampus, thus enhancing the labeling accuracy. Second, a novel forest selection strategy is proposed, such that each voxel in the test image can automatically select a set of optimal forests, and then dynamically fuses their respective outputs for determining the final label. Furthermore, we enhance the spatially-localized random forests with the aid of the auto-context strategy. In this way, our proposed learning framework can gradually refine the tentative labeling result for better performance. Experiments show that, regarding the large datasets of both adult and infant brain MR images, our method owns satisfactory scalability by segmenting the hippocampus accurately and efficiently
Automated Atlas-Based Segmentation of Brain Structures in MR Images: Application to a Population-Based Imaging Study
The final type of segmentationmethod is atlas-based segmentation (sometimes also
called label propagation). In this approach, additional knowledge is introduced
through an atlas image, in which an expert has labeled the brain structures of interest.
The atlas is first registered to the target image, and the resulting transformation
is then used to deform the atlas labels to the coordinate system of the target image.
During registration the similarity between the warped atlas image and the
target image is maximized, while at the same time the deformation is constrained
to ensure that the spatial information of the atlas is maintained
Multiatlas-Based Segmentation Editing With Interaction-Guided Patch Selection and Label Fusion
We propose a novel multi-atlas based segmentation method to address the segmentation editing scenario, where an incomplete segmentation is given along with a set of existing reference label images (used as atlases). Unlike previous multi-atlas based methods, which depend solely on appearance features, we incorporate interaction-guided constraints to find appropriate atlas label patches in the reference label set and derive their weights for label fusion. Specifically, user interactions provided on the erroneous parts are first divided into multiple local combinations. For each combination, the atlas label patches well-matched with both interactions and the previous segmentation are identified. Then, the segmentation is updated through the voxel-wise label fusion of selected atlas label patches with their weights derived from the distances of each underlying voxel to the interactions. Since the atlas label patches well-matched with different local combinations are used in the fusion step, our method can consider various local shape variations during the segmentation update, even with only limited atlas label images and user interactions. Besides, since our method does not depend on either image appearance or sophisticated learning steps, it can be easily applied to general editing problems. To demonstrate the generality of our method, we apply it to editing segmentations of CT prostate, CT brainstem, and MR hippocampus, respectively. Experimental results show that our method outperforms existing editing methods in all three data sets
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