Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (3): 640-647.doi: 10.13229/j.cnki.jdxbgxb20211274

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Medical image segmentation based on multi⁃scale context⁃aware and semantic adaptor

Xue WANG1,2(),Zhan-shan LI1,2,Ying-da LYU3()   

  1. 1.College of Computer Science and Technology，Jilin University，Changchun 130012，China
    2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education，Jilin University，Changchun 130012，China
    3.Center for Computer Fundamental Education，Jilin University，Changchun 130012，China
  • Received:2021-11-25 Online:2022-03-01 Published:2022-03-08
  • Contact: Ying-da LYU E-mail:wxue19@mails.jlu.edu.cn;ydlv@jlu.edu.cn

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Abstract:

Due to the complex characteristics of medical images， for example， the lesion region possesses an irregular shape and its scale can greatly vary， the intensity of surrounding tissues is inhomogeneous and the boundary is blurred， which reduce the accuracy of medical image segmentation， a medical image segmentation algorithm based on multi-scale context-aware and semantic adaptor is proposed. In order to improve the representation ability of feature learning， the multi-scale context-aware module is utilized to learn rich context information from multiple receptive fields， and dynamically assign the weight of semantic features at different scales according to the size of the target region. The multi-level semantic adaptor module is adopted to aggregate multi-level abstract semantic features and spatial details to refine the boundary of the target region and reduce the feature gaps between encoders and decoders. The algorithm proposed in this paper is compared with other algorithms quantitatively and qualitatively on three public medical image datasets of different modalities. The experimental results show that the proposed algorithm is superior to other algorithms in various complex scenarios of medical image segmentation tasks.

Key words: computer application, medical image segmentation, multi-scale context-aware, semantic adaptor, context information

CLC Number: 

  • TP391

Fig.1

Architecture of multi-scale context-aware and semantic adaptor encoder-decoder network"

Fig.2

Architecture of multi-scale context-aware module"

Fig.3

Architecture of multi-level semanticadaptor module"

Table 1

Detailed description of datasets"

数据集图片数量图片大小模态
ISIC 20182594变化尺寸皮肤镜图像
Kvasir-SEG［22］1000变化尺寸结肠镜图像
2018 Data Science Bowl670256 × 256混合模态

Table 2

Experimental comparison results on ISIC 2018 dataset"

方法F1RecSpecAcc
U-Net［12］0.81630.81920.97410.9391
BCDU-Net（d=1）［13］0.84700.78300.98000.9360
BCDU-Net（d=3）［13］0.85100.78500.98200.9370
FANet［23］0.87310.86500.96110.9351
本文0.86960.88370.97830.9591

Fig.4

Visualized results on ISIC 2018 skinlesion segmentation"

Table 3

Comparison results on Kvasir-SEG dataset"

方法DiceMIoURecPrec
U-Net［12］0.81160.72170.79490.8726
ResUNet-mod［25］0.79090.42870.69090.8713
ResUNet++［24］0.81330.79270.70640.8774
本文0.86300.87130.85130.9053

Fig.5

Visualized results on Kvasir-SEGpolyp segmentation"

Table 4

Comparison results on 2018 Data Science Bowl dataset"

方法DiceMIoURecPrec
U-Net［12］0.90980.83720.89040.9164
DoubleU-Net［19］0.91330.84070.64070.9496
FANet［23］0.91760.85690.92220.9194
本文0.92370.91800.90220.9589

Fig.6

Visualized results on 2018 Data ScienceBowl nuclear segmentation"

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