209 research outputs found
Multi-Scale Convolutional-Stack Aggregation for Robust White Matter Hyperintensities Segmentation
Segmentation of both large and small white matter hyperintensities/lesions in
brain MR images is a challenging task which has drawn much attention in recent
years. We propose a multi-scale aggregation model framework to deal with
volume-varied lesions. Firstly, we present a specifically-designed network for
small lesion segmentation called Stack-Net, in which multiple convolutional
layers are connected, aiming to preserve rich local spatial information of
small lesions before the sub-sampling layer. Secondly, we aggregate multi-scale
Stack-Nets with different receptive fields to learn multi-scale contextual
information of both large and small lesions. Our model is evaluated on recent
MICCAI WMH Challenge Dataset and outperforms the state-of-the-art on lesion
recall and lesion F1-score under 5-fold cross validation. In addition, we
further test our pre-trained models on a Multiple Sclerosis lesion dataset with
30 subjects under cross-center evaluation. Results show that the aggregation
model is effective in learning multi-scale spatial information.It claimed the
first place on the hidden test set after independent evaluation by the
challenge organizer. In addition, we further test our pre-trained models on a
Multiple Sclerosis lesion dataset with 30 subjects under cross-center
evaluation. Results show that the aggregation model is effective in learning
multi-scale spatial information.Comment: accepted by MICCAI brain lesion worksho
AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning
Perfusion imaging is crucial in acute ischemic stroke for quantifying the
salvageable penumbra and irreversibly damaged core lesions. As such, it helps
clinicians to decide on the optimal reperfusion treatment. In perfusion CT
imaging, deconvolution methods are used to obtain clinically interpretable
perfusion parameters that allow identifying brain tissue abnormalities.
Deconvolution methods require the selection of two reference vascular functions
as inputs to the model: the arterial input function (AIF) and the venous output
function, with the AIF as the most critical model input. When manually
performed, the vascular function selection is time demanding, suffers from poor
reproducibility and is subject to the professionals' experience. This leads to
potentially unreliable quantification of the penumbra and core lesions and,
hence, might harm the treatment decision process. In this work we automatize
the perfusion analysis with AIFNet, a fully automatic and end-to-end trainable
deep learning approach for estimating the vascular functions. Unlike previous
methods using clustering or segmentation techniques to select vascular voxels,
AIFNet is directly optimized at the vascular function estimation, which allows
to better recognise the time-curve profiles. Validation on the public ISLES18
stroke database shows that AIFNet reaches inter-rater performance for the
vascular function estimation and, subsequently, for the parameter maps and core
lesion quantification obtained through deconvolution. We conclude that AIFNet
has potential for clinical transfer and could be incorporated in perfusion
deconvolution software.Comment: Preprint submitted to Elsevie
A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images
Collateral circulation results from specialized anastomotic channels which are capable of providing oxygenated blood to regions with compromised blood flow caused by ischemic injuries. The quality of collateral circulation has been established as a key factor in determining the likelihood of a favorable clinical outcome and goes a long way to determine the choice of stroke care model - that is the decision to transport or treat eligible patients immediately.
Though there exist several imaging methods and grading criteria for quantifying collateral blood flow, the actual grading is mostly done through manual inspection of the acquired images. This approach is associated with a number of challenges. First, it is time-consuming - the clinician needs to scan through several slices of images to ascertain the region of interest before deciding on what severity grade to assign to a patient. Second, there is a high tendency for bias and inconsistency in the final grade assigned to a patient depending on the experience level of the clinician.
We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data. First, we formulate a region of interest detection task as a reinforcement learning problem and train a deep learning network to automatically detect the occluded region within the 3D MR perfusion volumes. Second, we extract radiomic features from the obtained region of interest through local image descriptors and denoising auto-encoders. Finally, we apply a convolutional neural network and other machine learning classifiers to the extracted radiomic features to automatically predict the collateral flow grading of the given patient volume as one of three severity classes - no flow (0), moderate flow (1), and good flow (2)..
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