1,747 research outputs found
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
Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images
The demons algorithm is a fast non-parametric non-rigid registration method. In recent years great efforts have been made to improve the approach; the state of the art version yields symmetric inverse-consistent largedeformation diffeomorphisms. However, only limited work has explored inter-modal similarity metrics, with no practical evaluation on multi-modality data. We present a diffeomorphic demons implementation using the analytical gradient of Normalised Mutual Information (NMI) in a conjugate gradient optimiser. We report the first qualitative and quantitative assessment of the demons for inter-modal registration. Experiments to spatially normalise real MR images, and to recover simulated deformation fields, demonstrate (i) similar accuracy from NMI-demons and classical demons when the latter may be used, and (ii) similar accuracy for NMI-demons on T1w-T1w and T1w-T2w registration, demonstrating its potential in multi-modal scenarios
Longitudinal multivariate tensor- and searchlight-based morphometry using permutation testing
Tensor based morphometry [1] was used to detect
statistically significant regions of neuroanatomical
change over time in a comparison between 36 probable
Alzheimer's Disease patients and 20 age- and sexmatched
controls. Baseline and twelve-month repeat
Magnetic Resonance images underwent tied spatial
normalisation [10] and longitudinal high-dimensional
warps were then estimated. Analyses involved univariate
and multivariate data derived from the longitudinal
deformation fields. The most prominent findings were
expansion of the fluid spaces, and contraction of the
hippocampus and temporal region. Multivariate measures
were notably more powerful, and have the potential to
identify patterns of morphometric difference that would
be overlooked by conventional mass-univariate analysis
Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
Deep-learning has proved in recent years to be a powerful tool for image
analysis and is now widely used to segment both 2D and 3D medical images.
Deep-learning segmentation frameworks rely not only on the choice of network
architecture but also on the choice of loss function. When the segmentation
process targets rare observations, a severe class imbalance is likely to occur
between candidate labels, thus resulting in sub-optimal performance. In order
to mitigate this issue, strategies such as the weighted cross-entropy function,
the sensitivity function or the Dice loss function, have been proposed. In this
work, we investigate the behavior of these loss functions and their sensitivity
to learning rate tuning in the presence of different rates of label imbalance
across 2D and 3D segmentation tasks. We also propose to use the class
re-balancing properties of the Generalized Dice overlap, a known metric for
segmentation assessment, as a robust and accurate deep-learning loss function
for unbalanced tasks
Diffeomorphic Demons using Normalised Mutual Information, Evaluation on Multi-Modal Brain MR Images
The demons algorithm is a fast non-parametric non-rigid registration method. In recent years great efforts have been made to improve the approach; the state of the art version yields symmetric inverse-consistent large-deformation diffeomorphisms. However, only limited work has explored inter-modal similarity metrics, with no practical evaluation on multi-modality data. We present a diffeomorphic demons implementation using the analytical gradient of Normalised Mutual Information (NMI) in a conjugate gradient optimiser. We report the first qualitative and quantitative assessment of the demons for inter-modal registration. Experiments to spatially normalise real MR images, and to recover simulated deformation fields, demonstrate (i) similar accuracy from NMI-demons and classical demons when the latter may be used, and (ii) similar accuracy for NMI-demons on T1w-T1w and T1w-T2w registration, demonstrating its potential in multi-modal scenarios
Disease Progression Modeling and Prediction through Random Effect Gaussian Processes and Time Transformation
The development of statistical approaches for the joint modelling of the
temporal changes of imaging, biochemical, and clinical biomarkers is of
paramount importance for improving the understanding of neurodegenerative
disorders, and for providing a reference for the prediction and quantification
of the pathology in unseen individuals. Nonetheless, the use of disease
progression models for probabilistic predictions still requires investigation,
for example for accounting for missing observations in clinical data, and for
accurate uncertainty quantification. We tackle this problem by proposing a
novel Gaussian process-based method for the joint modeling of imaging and
clinical biomarker progressions from time series of individual observations.
The model is formulated to account for individual random effects and time
reparameterization, allowing non-parametric estimates of the biomarker
evolution, as well as high flexibility in specifying correlation structure, and
time transformation models. Thanks to the Bayesian formulation, the model
naturally accounts for missing data, and allows for uncertainty quantification
in the estimate of evolutions, as well as for probabilistic prediction of
disease staging in unseen patients. The experimental results show that the
proposed model provides a biologically plausible description of the evolution
of Alzheimer's pathology across the whole disease time-span as well as
remarkable predictive performance when tested on a large clinical cohort with
missing observations.Comment: 13 pages, 2 figure
Part-to-whole Registration of Histology and MRI using Shape Elements
Image registration between histology and magnetic resonance imaging (MRI) is
a challenging task due to differences in structural content and contrast. Too
thick and wide specimens cannot be processed all at once and must be cut into
smaller pieces. This dramatically increases the complexity of the problem,
since each piece should be individually and manually pre-aligned. To the best
of our knowledge, no automatic method can reliably locate such piece of tissue
within its respective whole in the MRI slice, and align it without any prior
information. We propose here a novel automatic approach to the joint problem of
multimodal registration between histology and MRI, when only a fraction of
tissue is available from histology. The approach relies on the representation
of images using their level lines so as to reach contrast invariance. Shape
elements obtained via the extraction of bitangents are encoded in a
projective-invariant manner, which permits the identification of common pieces
of curves between two images. We evaluated the approach on human brain
histology and compared resulting alignments against manually annotated ground
truths. Considering the complexity of the brain folding patterns, preliminary
results are promising and suggest the use of characteristic and meaningful
shape elements for improved robustness and efficiency.Comment: Paper accepted at ICCV Workshop (Bio-Image Computing
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