Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation

• URL: http://arxiv.org/abs/2004.02009v1
• Date: Sat, 4 Apr 2020 20:09:06 GMT
• Title: Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation
• Authors: Mehrdad Noori, Ali Bahri and Karim Mohammadi
• Abstract summary: Gliomas are the most common and aggressive among brain tumors, which cause a short life expectancy in their highest grade. Deep convolutional neural networks (DCNNs) have achieved a remarkable performance in brain tumor segmentation. However, this task is still difficult owing to high varying intensity and appearance of gliomas.
• Score: 2.371982686172067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gliomas are the most common and aggressive among brain tumors, which cause a short life expectancy in their highest grade. Therefore, treatment assessment is a key stage to enhance the quality of the patients' lives. Recently, deep convolutional neural networks (DCNNs) have achieved a remarkable performance in brain tumor segmentation, but this task is still difficult owing to high varying intensity and appearance of gliomas. Most of the existing methods, especially UNet-based networks, integrate low-level and high-level features in a naive way, which may result in confusion for the model. Moreover, most approaches employ 3D architectures to benefit from 3D contextual information of input images. These architectures contain more parameters and computational complexity than 2D architectures. On the other hand, using 2D models causes not to benefit from 3D contextual information of input images. In order to address the mentioned issues, we design a low-parameter network based on 2D UNet in which we employ two techniques. The first technique is an attention mechanism, which is adopted after concatenation of low-level and high-level features. This technique prevents confusion for the model by weighting each of the channels adaptively. The second technique is the Multi-View Fusion. By adopting this technique, we can benefit from 3D contextual information of input images despite using a 2D model. Experimental results demonstrate that our method performs favorably against 2017 and 2018 state-of-the-art methods.

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