In this paper, we propose a fully convolutional neural network (CNN) model to segment contusions and lesions from brain magnetic resonance (MR) images of ...
In this paper, we propose a fully convolutional neural network (CNN) model to segment contusions and lesions from brain magnetic resonance (MR) images of ...
Thus, CNNs have the potential to classify and detect TBI using data from MRI, CT scans, calcium imaging, and electroencephalogram (EEG). Also, CNN models have ...
Abstract: Traumatic brain injury (TBI) is caused by a sudden trauma to the head that may result in hematomas and contusions and can lead to stroke or ...
Jul 27, 2018 · In this paper, we propose a fully convolutional neural net- work (CNN) model to segment contusions and lesions from brain magnetic resonance (MR) ...
We assessed the progression of hippocampal damage after TBI by using our automatic segmentation tool. Our data show that the presence of TBI, time after TBI, ...
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Mar 16, 2023 · Kamnitsas et al. (2017) proposed a 3D convolutional neural network (CNN) for segmentation of TBI lesions, brain tumors, and ischemic stroke ...
Our aim was to use 2D convolutional neural networks for automatic segmentation of the spinal cord and traumatic contusion injury from axial T2-weighted MR ...
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What is brain region segmentation using convolutional neural network?
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Aug 30, 2024 · To generate a contusion label map for each participant, we used the recently described BLAST-CT algorithm. BLAST-CT is a convolutional neural ...
Nov 22, 2019 · We develop a convolutional neural network (CNN) to estimate regional brain strains instantly and accurately by conceptualizing head rotational velocity ...