DPA‐UNet rectal cancer image segmentation based on visual attention

Summary Low segmentation accuracy and background noise exisit in rectal cancer lesion segmentation. To segment colorectal tumors in CT images accurately, we propose an improved U‐Net network for colorectal tumor image segmentation dilated‐pyramid‐attention U‐Net (DPA‐UNet). In this paper, we use the...

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Bibliographic Details
Published in:Concurrency and computation 2023-09, Vol.35 (21), p.n/a
Main Authors: Wang, Yuqian, Ma, JianWei, Sergey, Axyonov, Zang, Shaofei, Zhang, Miao
Format: Article
Language:English
Subjects:
attention
Background noise
Cancer
colorectal cancer
Computed tomography
dilated convolution
Feature maps
Image segmentation
Medical imaging
Pixels
Rectum
Tumors
U‐Net
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Summary:Summary Low segmentation accuracy and background noise exisit in rectal cancer lesion segmentation. To segment colorectal tumors in CT images accurately, we propose an improved U‐Net network for colorectal tumor image segmentation dilated‐pyramid‐attention U‐Net (DPA‐UNet). In this paper, we use the U‐Net network and combine techniques such as dilated convolution, weighted feature pyramid structure (W‐FPN), and convolutional block attention module (CBAM) mechanism. Firstly, CBAM and W‐FPN are combined to extract dense pixel‐level features for pixel labeling. Secondly, after the third network output layer, three serially dilated depth‐separable dilated convolutional layers with dilation rates of 1, 2, and 4, are added respectively to expand the feature map receptive field. Finally, the DPA‐UNet model is compared and analyzed with other new network structures. The experimental results show that DPA‐UNet achieves automatic segmentation of the colorectal cancer image region of interest (ROI).
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.7670