Chaofan Du and Peter Xiaoping Liu
Medical imaging robots, MRI images, brain tumour segmentation, lightweight network
Medical imaging robots typically use technologies, such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT), to generate images of the human body interior. These generated images are complex and contain a large amount of noise and interference, which requires high-precision and real-time fast image analysis algorithms to extract significant information, including tumour area, tumour location, organ and tissue, and blood vessel information. This paper proposes a novel lightweight neural network to perform tumour segmentation in brain MRI images, which could realize the high-accuracy and fast execution. To meet the real-time requirements, a lightweight module based on channel attention mechanism is presented, which constitutes an encoder–decoder architecture for the segmentation task. To enrich the feature map information, this paper designs a spatial attention mechanism to concatenate the output feature maps of the encoder and decoder correspondingly, which could realize the better fusion of high-level and low-level semantic features extracted by the network. The comparison experiments and ablation studies are conducted to improve the effectiveness of the proposed model, which could represent a higher performance. The computational cost of the proposed model shows the possibility of a real-time implementation.
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