53 research outputs found
A Review on Computer Aided Diagnosis of Acute Brain Stroke.
Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas
Concurrent ischemic lesion age estimation and segmentation of CT brain using a transformer-based network
The cornerstone of stroke care is expedient management that varies depending on the time since stroke onset. Consequently, clinical decision making is centered on accurate knowledge of timing and often requires a radiologist to interpret Computed Tomography (CT) of the brain to confirm the occurrence and age of an event. These tasks are particularly challenging due to the subtle expression of acute ischemic lesions and the dynamic nature of their appearance. Automation efforts have not yet applied deep learning to estimate lesion age and treated these two tasks independently, so, have overlooked their inherent complementary relationship. To leverage this, we propose a novel end-to-end multi-task transformer-based network optimized for concurrent segmentation and age estimation of cerebral ischemic lesions. By utilizing gated positional self-attention and CT-specific data augmentation, the proposed method can capture long-range spatial dependencies while maintaining its ability to be trained from scratch under low-data regimes commonly found in medical imaging. Furthermore, to better combine multiple predictions, we incorporate uncertainty by utilizing quantile loss to facilitate estimating a probability density function of lesion age. The effectiveness of our model is then extensively evaluated on a clinical dataset consisting of 776 CT images from two medical centers. Experimental results demonstrate that our method obtains promising performance, with an area under the curve (AUC) of 0.933 for classifying lesion ages ≤4.5 hours compared to 0.858 using a conventional approach, and outperforms task-specific state-of-the-art algorithms
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
Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection
Time is a fundamental factor during stroke treatments. A fast, automatic approach that segmentsthe ischemic regions helps treatment decisions. In clinical use today, a set of color-coded parametric maps generated from computed tomography perfusion (CTP) images are investigated manually to decide a treatment plan. We propose an automatic method based on a neural network using a set of parametric maps to segment the two ischemic regions (core and penumbra) in patients affected by acute ischemic stroke. Our model is based on a convolution-deconvolution bottleneck structure with multi-input and slow fusion. A loss function based on the focal Tversky index addresses the data imbalance issue. The proposed architecture demonstrates effective performance and results comparable to the ground truth annotated by neuroradiologists. A Dice coefficient of 0.81 for penumbra and 0.52 for core over the large vessel occlusion test set is achieved. The full implementation is available at: https://git.io/JtFGb.publishedVersio
CT Perfusion is All We Need: 4D CNN Segmentation of Penumbra and Core in Patient With Suspected Ischemic Stroke
Precise and fast prediction methods for ischemic areas comprised of dead
tissue, core, and salvageable tissue, penumbra, in acute ischemic stroke (AIS)
patients are of significant clinical interest. They play an essential role in
improving diagnosis and treatment planning. Computed Tomography (CT) scan is
one of the primary modalities for early assessment in patients with suspected
AIS. CT Perfusion (CTP) is often used as a primary assessment to determine
stroke location, severity, and volume of ischemic lesions. Current automatic
segmentation methods for CTP mostly use already processed 3D parametric maps
conventionally used for clinical interpretation by radiologists as input.
Alternatively, the raw CTP data is used on a slice-by-slice basis as 2D+time
input, where the spatial information over the volume is ignored. In addition,
these methods are only interested in segmenting core regions, while predicting
penumbra can be essential for treatment planning. This paper investigates
different methods to utilize the entire 4D CTP as input to fully exploit the
spatio-temporal information, leading us to propose a novel 4D convolution
layer. Our comprehensive experiments on a local dataset of 152 patients divided
into three groups show that our proposed models generate more precise results
than other methods explored. Adopting the proposed 4D mJ-Net, a Dice
Coefficient of 0.53 and 0.23 is achieved for segmenting penumbra and core
areas, respectively. The code is available on
https://github.com/Biomedical-Data-Analysis-Laboratory/4D-mJ-Net.git
Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection
Time is a fundamental factor during stroke treatments. A fast, automatic
approach that segments the ischemic regions helps treatment decisions. In
clinical use today, a set of color-coded parametric maps generated from
computed tomography perfusion (CTP) images are investigated manually to decide
a treatment plan. We propose an automatic method based on a neural network
using a set of parametric maps to segment the two ischemic regions (core and
penumbra) in patients affected by acute ischemic stroke. Our model is based on
a convolution-deconvolution bottleneck structure with multi-input and slow
fusion. A loss function based on the focal Tversky index addresses the data
imbalance issue. The proposed architecture demonstrates effective performance
and results comparable to the ground truth annotated by neuroradiologists. A
Dice coefficient of 0.81 for penumbra and 0.52 for core over the large vessel
occlusion test set is achieved. The full implementation is available at:
https://git.io/JtFGb
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