基于Swin-Transformer的颈动脉超声图像斑块分割

doi:10.16180/j.cnki.issn1007-7820.2024.09.008

Abstract

Abstract:

The evaluation of carotid ultrasound image plaque requires a large number of experienced clinicians, and the ultrasound image has the characteristics of blurred boundary and strong noise interference, making the evaluation of plaques time-consuming and laborious. Therefore, a fully automated carotid plaque segmentation method is urgently needed to solve the problem of manpower scarcity. This study proposes a deep neural network model based on Swin-Transformer (Shifted-Windows Transformer) block for the automatic segmentation of carotid plaques. Based on the U-Net(U-Convolutional Network) architecture, the encoding part uses three convolutional blocks for image down-sampling to obtain feature images of different resolution sizes, and then adds six pairs of two consecutive Swin-Transformer blocks for more refined feature extraction. The decoding part up-samples the refined features output by the Swin-Transformer module step by step, and jump-joints them with the feature maps of each resolution level in the encoding part, respectively. The comparison experiments based on the data set of Tong Ren Hospital show that the Dice index of the proposed deep neural network model reaches 0.814 2, which is higher than that of other comparison networks. The results demonstrate that the proposed model can effectively extract the features of carotid ultrasound image plaques and achieve automated and high-precision plaque segmentation.

Key words: carotid plaque, deep learning, ultrasound image, image processing, segmentation algorithm, medical image, U-shaped architecture, Swin-Transformer block

CLC Number:

TP391.41

HE Zhiqiang, SUN Zhanquan. Swin-Transformer-Based Carotid Ultrasound Image Plaque Segmentation[J].Electronic Science and Technology, 2024, 37(9): 48-56.

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References 26

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方法	Dice	Precision	Recall	IoU
Baseline	0.689 7	0.744 1	0.657 9	0.563 1
Baseline+12 Transformer	0.793 8	0.842 7	0.756 3	0.682 4
Baseline+6 S-Transformer	0.806 7	0.849 4	0.791 4	0.689 1
本文	0.814 2	0.852 2	0.796 4	0.703 0

方法	Dice	Recall	IoU
UNet	0.689 7	0.657 9	0.563 1
RefineNet	0.714 3	0.695 2	0.614 2
AttUnet	0.715 9	0.750 1	0.605 6
CeNet	0.742 1	0.689 2	0.635 8
DeeplabV3+	0.771 8	0.785 5	0.656 6
PspNet	0.779 5	0.768 5	0.662 1
DenseAspp	0.784 1	0.775 6	0.679 8
DeeplabV3	0.795 6	0.787 7	0.693 4
本文	0.814 2	0.796 4	0.703 0

Swin-Transformer-Based Carotid Ultrasound Image Plaque Segmentation

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