吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (3): 640-647.doi: 10.13229/j.cnki.jdxbgxb20211274

• 计算机科学与技术 • 上一篇    

基于多尺度感知和语义适配的医学图像分割算法

王雪1,2(),李占山1,2,吕颖达3()   

  1. 1.吉林大学 计算机科学与技术学院，长春 130012
    2.吉林大学 符号计算与知识工程教育部重点实验室，长春 130012
    3.吉林大学 公共计算机教学与研究中心，长春 130012
  • 收稿日期:2021-11-25 出版日期:2022-03-01 发布日期:2022-03-08
  • 通讯作者: 吕颖达 E-mail:wxue19@mails.jlu.edu.cn;ydlv@jlu.edu.cn
  • 作者简介:王雪（1982-），女，博士研究生. 研究方向：图像处理与模式识别. E-mail：wxue19@mails.jlu.edu.cn
  • 基金资助:
    国家重点研发计划项目(2018YFB0804202);国家自然科学基金区域联合基金项目(U19A2057);国家自然科学基金面上项目(61876070);吉林省科技发展计划项目(20190303134SF)

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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摘要：

针对医学图像中病灶区域的形状不规则、尺度变化大、强度不均匀和边界模糊等复杂特点导致医学图像分割精度下降的问题，本文提出了一种基于多尺度感知和语义适配的医学图像分割算法。通过多尺度上下文感知模块，从多个感受野学习目标区域丰富的上下文信息，并根据目标区域大小动态分配不同尺度语义特征的权重，以提高特征学习的表征能力。通过多层语义适配模块聚合多级抽象语义特征和空间细节信息，细化目标区域的边界，同时减少编解码器间的特征差异。将本文算法在3个不同模态的公开医学图像数据集上进行定量和定性对比，实验结果表明，本文算法在多个医学图像复杂场景分割中均优于其他算法。

关键词: 计算机应用, 医学图像分割, 多尺度上下文感知, 语义适配, 上下文信息

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

中图分类号: 

  • TP391

图1

多尺度感知和语义适配的深度编解码网络结构"

图2

多尺度上下文感知模块结构图"

图3

多层语义适配模块结构图"

表1

数据集细节描述"

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

表2

ISIC 2018数据集上的实验对比结果"

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

图4

ISIC 2018皮肤病变分割的可视化结果"

表3

Kvasir-SEG 数据集上的实验对比结果"

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

图5

Kvasir-SEG息肉分割的可视化结果"

表4

2018 Data Science Bowl数据集上的实验对比结果"

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

图6

2018 Data Science Bowl细胞核分割的可视化结果"

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