8,129 research outputs found
Automated Detection of Regions of Interest for Brain Perfusion MR Images
Images with abnormal brain anatomy produce problems for automatic
segmentation techniques, and as a result poor ROI detection affects both
quantitative measurements and visual assessment of perfusion data. This paper
presents a new approach for fully automated and relatively accurate ROI
detection from dynamic susceptibility contrast perfusion magnetic resonance and
can therefore be applied excellently in the perfusion analysis. In the proposed
approach the segmentation output is a binary mask of perfusion ROI that has
zero values for air pixels, pixels that represent non-brain tissues, and
cerebrospinal fluid pixels. The process of binary mask producing starts with
extracting low intensity pixels by thresholding. Optimal low-threshold value is
solved by obtaining intensity pixels information from the approximate
anatomical brain location. Holes filling algorithm and binary region growing
algorithm are used to remove falsely detected regions and produce region of
only brain tissues. Further, CSF pixels extraction is provided by thresholding
of high intensity pixels from region of only brain tissues. Each time-point
image of the perfusion sequence is used for adjustment of CSF pixels location.
The segmentation results were compared with the manual segmentation performed
by experienced radiologists, considered as the reference standard for
evaluation of proposed approach. On average of 120 images the segmentation
results have a good agreement with the reference standard. All detected
perfusion ROIs were deemed by two experienced radiologists as satisfactory
enough for clinical use. The results show that proposed approach is suitable to
be used for perfusion ROI detection from DSC head scans. Segmentation tool
based on the proposed approach can be implemented as a part of any automatic
brain image processing system for clinical use
Noise-robust method for image segmentation
Segmentation of noisy images is one of the most challenging problems in image analysis and any improvement of segmentation methods can highly influence the performance of many image processing applications. In automated image segmentation, the fuzzy c-means (FCM) clustering has been widely used because of its ability to model uncertainty within the data, applicability to multi-modal data and fairly robust behaviour. However, the standard FCM algorithm does not consider any information about the spatial linage context and is highly sensitive to noise and other imaging artefacts. Considering above mentioned problems, we developed a new FCM-based approach for the noise-robust fuzzy clustering and we present it in this paper. In this new iterative algorithm we incorporated both spatial and feature space information into the similarity measure and the membership function. We considered that spatial information depends on the relative location and features of the neighbouring pixels. The performance of the proposed algorithm is tested on synthetic image with different noise levels and real images. Experimental quantitative and qualitative segmentation results show that our method efficiently preserves the homogeneity of the regions and is more robust to noise than other FCM-based methods
Unsupervised Lesion Detection via Image Restoration with a Normative Prior
Unsupervised lesion detection is a challenging problem that requires
accurately estimating normative distributions of healthy anatomy and detecting
lesions as outliers without training examples. Recently, this problem has
received increased attention from the research community following the advances
in unsupervised learning with deep learning. Such advances allow the estimation
of high-dimensional distributions, such as normative distributions, with higher
accuracy than previous methods.The main approach of the recently proposed
methods is to learn a latent-variable model parameterized with networks to
approximate the normative distribution using example images showing healthy
anatomy, perform prior-projection, i.e. reconstruct the image with lesions
using the latent-variable model, and determine lesions based on the differences
between the reconstructed and original images. While being promising, the
prior-projection step often leads to a large number of false positives. In this
work, we approach unsupervised lesion detection as an image restoration problem
and propose a probabilistic model that uses a network-based prior as the
normative distribution and detect lesions pixel-wise using MAP estimation. The
probabilistic model punishes large deviations between restored and original
images, reducing false positives in pixel-wise detections. Experiments with
gliomas and stroke lesions in brain MRI using publicly available datasets show
that the proposed approach outperforms the state-of-the-art unsupervised
methods by a substantial margin, +0.13 (AUC), for both glioma and stroke
detection. Extensive model analysis confirms the effectiveness of MAP-based
image restoration.Comment: Extended version of 'Unsupervised Lesion Detection via Image
Restoration with a Normative Prior' (MIDL2019
The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brainâbehavior relationships after stroke
The goal of the Enhancing Neuroimaging Genetics through MetaâAnalysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using wellâpowered metaâ and megaâanalytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and largeâscale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided
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