Abstract
Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-weighted MR imaging (DWI) is sensitive to these lesions, localizing and quantifying them manually is costly and challenging for clinicians. In this paper, we propose a novel framework to automatically segment stroke lesions in DWI. Our framework consists of two convolutional neural networks (CNNs): one is an ensemble of two DeconvNets (Noh et al., 2015), which is the EDD Net; the second CNN is the multi-scale convolutional label evaluation net (MUSCLE Net), which aims to evaluate the lesions detected by the EDD Net in order to remove potential false positives. To the best of our knowledge, it is the first attempt to solve this problem and using both CNNs achieves very good results. Furthermore, we study the network architectures and key configurations in detail to ensure the best performance. It is validated on a large dataset comprising clinical acquired DW images from 741 subjects. A mean accuracy of Dice coefficient obtained is 0.67 in total. The mean Dice scores based on subjects with only small and large lesions are 0.61 and 0.83, respectively. The lesion detection rate achieved is 0.94.
Highlights
Stroke is one of the major causes of long-term disability and death globally (Lopez et al, 2006)
Its mean Dice score was 9% higher than the best baseline convolutional neural networks (CNNs). This improvement came from the significant reduction of the number of false positives as its m#false negatives (FN), mSFP, and mSFN were similar to the baselines'
The m#FN of the MUSCLE Net only increased a bit compared to the EDD Net, which indicated that it maintained most of the true positive lesions
Summary
Stroke is one of the major causes of long-term disability and death globally (Lopez et al, 2006). A number of factors such as energy depletion and cell death are thought to lead to ischemic brain injuries (Dirnagl et al, 1999). Brain imaging is one of the most important methods to assess patients suffering from ischemic stroke (van der Worp and van Gijn, 2007) and computed tomography (CT) and magnetic resonance imaging (MRI) are usually acquired (Latchaw et al, 2009). CT is more widely used because it is faster and less expensive while MRI has much higher sensitivity for the acute ischemic lesions (Lansberg et al, 2000). Diffusion-weighted MR imaging (DWI) has advantages in diagnosis of acute ischemic lesion in the early stage
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