基于多尺度感知和语义适配的医学图像分割算法

doi:10.13229/j.cnki.jdxbgxb20211274

Abstract

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

Xue WANG,Zhan-shan LI,Ying-da LYU. Medical image segmentation based on multi⁃scale context⁃aware and semantic adaptor[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 640-647.

Figures/Tables 10

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数据集	图片数量	图片大小	模态
ISIC 2018	2594	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 Bowl	670	256 × 256	混合模态

方法	F1	Rec	Spec	Acc
U-Net^［12］	0.8163	0.8192	0.9741	0.9391
BCDU-Net（d=1）^［13］	0.8470	0.7830	0.9800	0.9360
BCDU-Net（d=3）^［13］	0.8510	0.7850	0.9820	0.9370
FANet^［23］	0.8731	0.8650	0.9611	0.9351
本文	0.8696	0.8837	0.9783	0.9591

方法	Dice	MIoU	Rec	Prec
U-Net^［12］	0.8116	0.7217	0.7949	0.8726
ResUNet-mod^［25］	0.7909	0.4287	0.6909	0.8713
ResUNet++^［24］	0.8133	0.7927	0.7064	0.8774
本文	0.8630	0.8713	0.8513	0.9053

方法	Dice	MIoU	Rec	Prec
U-Net^［12］	0.9098	0.8372	0.8904	0.9164
DoubleU-Net^［19］	0.9133	0.8407	0.6407	0.9496
FANet^［23］	0.9176	0.8569	0.9222	0.9194
本文	0.9237	0.9180	0.9022	0.9589

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

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