Abstract
Medical image segmentation is a fundamental technology for computer-aided diagnosis and clinical disease monitoring. Most of existing deep learning-based methods solely focus on the region and position of objects without considering edge information which provides accurate contour of objects and is beneficial to medical image segmentation. In this paper, we propose a novel Global-Local fusion UNet model (GLUNet) to address above problem, which contains a Global Attention Module (GAM) and a Local Edge Detection Module (LEDM). In GAM, we embed residual block and convolution block attention module to capture contextual and spatial information of objects. Meanwhile, to obtain accurate edge information of objects in medical image segmentation, we devise the LEDM to integrate edge information into our model. We also propose a multi-task loss function that combines the segmentation loss and the edge loss together to train our GLUNet. Experimental results demonstrate that our proposed method outperforms the original U-Net method and other state-of-the-art methods for lung segmentation in Computed Tomography (CT) images, cell/nuclei segmentation and vessel segmentation in retinal images.
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Wang, N., Quan, H. (2021). GLUNet: Global-Local Fusion U-Net for 2D Medical Image Segmentation. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12894. Springer, Cham. https://doi.org/10.1007/978-3-030-86380-7_7
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