10,139 research outputs found
Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks
Segmentation of both white matter lesions and deep grey matter structures is
an important task in the quantification of magnetic resonance imaging in
multiple sclerosis. Typically these tasks are performed separately: in this
paper we present a single segmentation solution based on convolutional neural
networks (CNNs) for providing fast, reliable segmentations of multimodal
magnetic resonance images into lesion classes and normal-appearing grey- and
white-matter structures. We show substantial, statistically significant
improvements in both Dice coefficient and in lesion-wise specificity and
sensitivity, compared to previous approaches, and agreement with individual
human raters in the range of human inter-rater variability. The method is
trained on data gathered from a single centre: nonetheless, it performs well on
data from centres, scanners and field-strengths not represented in the training
dataset. A retrospective study found that the classifier successfully
identified lesions missed by the human raters.
Lesion labels were provided by human raters, while weak labels for other
brain structures (including CSF, cortical grey matter, cortical white matter,
cerebellum, amygdala, hippocampus, subcortical GM structures and choroid
plexus) were provided by Freesurfer 5.3. The segmentations of these structures
compared well, not only with Freesurfer 5.3, but also with FSL-First and
Freesurfer 6.0
Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
Deep learning for regression tasks on medical imaging data has shown
promising results. However, compared to other approaches, their power is
strongly linked to the dataset size. In this study, we evaluate
3D-convolutional neural networks (CNNs) and classical regression methods with
hand-crafted features for survival time regression of patients with high grade
brain tumors. The tested CNNs for regression showed promising but unstable
results. The best performing deep learning approach reached an accuracy of
51.5% on held-out samples of the training set. All tested deep learning
experiments were outperformed by a Support Vector Classifier (SVC) using 30
radiomic features. The investigated features included intensity, shape,
location and deep features. The submitted method to the BraTS 2018 survival
prediction challenge is an ensemble of SVCs, which reached a cross-validated
accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set,
and 42.9% on the testing set. The results suggest that more training data is
necessary for a stable performance of a CNN model for direct regression from
magnetic resonance images, and that non-imaging clinical patient information is
crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation
(BraTS) Challenge 2018, survival prediction tas
Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging
Many analyses of neuroimaging data involve studying one or more regions of
interest (ROIs) in a brain image. In order to do so, each ROI must first be
identified. Since every brain is unique, the location, size, and shape of each
ROI varies across subjects. Thus, each ROI in a brain image must either be
manually identified or (semi-) automatically delineated, a task referred to as
segmentation. Automatic segmentation often involves mapping a previously
manually segmented image to a new brain image and propagating the labels to
obtain an estimate of where each ROI is located in the new image. A more recent
approach to this problem is to propagate labels from multiple manually
segmented atlases and combine the results using a process known as label
fusion. To date, most label fusion algorithms either employ voting procedures
or impose prior structure and subsequently find the maximum a posteriori
estimator (i.e., the posterior mode) through optimization. We propose using a
fully Bayesian spatial regression model for label fusion that facilitates
direct incorporation of covariate information while making accessible the
entire posterior distribution. We discuss the implementation of our model via
Markov chain Monte Carlo and illustrate the procedure through both simulation
and application to segmentation of the hippocampus, an anatomical structure
known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure
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