Fully Automated Segmentation on Brain Ischemic and White Matter Hyperintensities Lesions using Semantic Segmentation Networks with Squeeze-and-Excitation Blocks in MRI

A B S T R A C T Ischemic stroke, a common disease in the elderly, can cause long-term disability and death. Quantitative measurement of brain ischemic lesions at the acute stage is vital for accurate diagnosis and treatment decisions for stroke patients. Currently, the manual segmentation of ischemic lesions is time-consuming, and it is difficult to extract potentially quantifiable information. These limitations can be overcome via deep learning, which has recently become a popular method to segment brain ischemic lesions in MRI. Fully automatic segmentation methods using deep learning quantitatively evaluate the infarct lesions and accurately identify lesions, localize fast lesions, and improve treatment planning. The state-of-the-art methods are variants of U-Net and are fully convolutional networks (FCN); however, these methods have a few limitations, including their inability to capture global features because of the encoder- and decoder- networks. To overcome this limitation, a semantic segmentation method using U-Net with squeeze-and-excitation (SE) blocks is proposed in this study. This method can improve channel-wise information with feature maps compared to a conventional network, to enhance the accuracy of brain lesion segmentation. Patients with acute infarction (N=429) were retrospectively enrolled in this study with the approval of IRB. Various types of semantic segmentation networks with or without SE blocks were developed, and the accuracies were compared with conventional in-house and commercial software. The Dice similarity coefficient (DSC) of U-Net and Dense U-Net with SE blocks was 85.39 ± 0.84 and 84.23 ± 1.60, respectively. The DSCs increase by 3.5% and 0.9% on average. Accuracies of basic U-Net and Dense U-Net with SE blocks were significantly better than those of conventional image processing methods and those without SE blocks (p-values: 1.0e-08, 2.272e-05, and 0.0003, respectively). To additionally evaluate the methods, a public dataset of white matter hyperintensities (WMHs) segmentation challenge at MICCAI 2017 was used. In the WMH dataset, the DSCs of U-Net with or without SE blocks were 74.64 ± 1.11 and 76.92 ± 0.78, respectively. The DSC increases by 2.3% on average. The semantic segmentation with SE blocks exhibited significantly better performances than those without SE blocks. The solution can be applied to measure various brain segmentation problems, including infarct volume (at the acute stage of stroke patients) and WMH volume.