Abstract
As a non-invasive, low-cost medical imaging technology, magnetic resonance imaging (MRI) has become an important tool for brain tumor diagnosis. Many scholars have carried out some related researches on MRI brain tumor segmentation based on deep convolutional neural networks, and have achieved good performance. However, due to the large spatial and structural variability of brain tumors and low image contrast, the segmentation of MRI brain tumors is challenging. Deep convolutional neural networks often lead to the loss of low-level details as the network structure deepens, and they cannot effectively utilize the multi-scale feature information. Therefore, a deep convolutional neural network with a multi-scale attention feature fusion module (MAFF-ResUNet) is proposed to address them. The MAFF-ResUNet consists of a U-Net with residual connections and a MAFF module. The combination of residual connections and skip connections fully retain low-level detailed information and improve the global feature extraction capability of the encoding block. Besides, the MAFF module selectively extracts useful information from the multi-scale hybrid feature map based on the attention mechanism to optimize the features of each layer and makes full use of the complementary feature information of different scales. The experimental results on the BraTs 2019 MRI dataset show that the MAFF-ResUNet can learn the edge structure of brain tumors better and achieve high accuracy.
Highlights
In daily life, the human brain is the controller of all behaviors and the sender of activity instructions
The task is to segment three nested subregions generated by the three labels (1, 2, and 4), named enhancing tumor (ET, the region of label 4), whole tumor (WT, the region consists of label 1, 2, and 4) and tumor core (TC, the region of label 1 and 4)
Inspired by U-Net (Ronneberger et al, 2015), ResNet (He et al, 2016), DAF (Wang et al, 2018), we propose a deep convolutional neural network with a multi-scale attention feature fusion module based on attention mechanism for brain tumor segmentation
Summary
The human brain is the controller of all behaviors and the sender of activity instructions. Zhang et al (2020) proposed a new type of densely connection inception convolutional neural network on the basis of U-Net architecture which was applied to medical images, and conducted experiments in tumor segmentation of brain MRI They added the Inception-Res module and the densely connecting convolutional module to increase the width and depth of the network, and at the same time led to an increase in the number of parameters, which slows down the speed of model training data (Angulakshmi and Deepa, 2021). A deep convolutional neural network composed of a U-Net and a multi-scale attention feature fusion module (MAFF) is proposed to achieve automatic segmentation of gliomas in 3D brain MRI images. (3) MAFF-ResUNet performs well on the public BraTs 2019 MRI dataset and has certain competitiveness in the field of brain tumor segmentation
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