Lung nodule segmentation algorithm based on full-scale channel feature aggregation coding and decoding network
10.3969/j.issn.1005-202X.2024.12.007
- VernacularTitle:基于全尺度通道特征聚合编解码网络的肺结节分割算法
- Author:
Shaopeng XIE
1
;
Mingquan WANG
1
;
Yujie GENG
1
;
Xinyue HUANG
1
;
Ran SHANG
1
Author Information
1. 中北大学信息与通信工程学院,山西 太原 030051
- Publication Type:Journal Article
- Keywords:
lung nodule segmentation;
full-scale skip connection;
binomial constraint loss function;
dilated convolution
- From:
Chinese Journal of Medical Physics
2024;41(12):1501-1508
- CountryChina
- Language:Chinese
-
Abstract:
To address the difficulty in accurately detecting pulmonary nodules of different properties,a full-scale channel feature aggregation encoding and decoding network(FCFA-Net)is employed to assist experienced physicians in diagnosis.The network which consists of SMC,full-scale feature aggregator,autocorrelation feature enhancer,channel feature hierarchy extraction decoder and binomial constraint loss function can fully extract shallow and deep features from CT images for realizing the segmentation of pulmonary nodules of different sizes and shapes.Compared with UNet,UNet++and TransUnet,FCFA-Net increases the accuracy by 9.96%,7.84%and 3.75%,recall rate by 5.50%,2.96%and 1.37%,mean intersection over union by 11.35%,7.16%and 4.18%,F1 score by 8.07%,5.87%and 3.10%,respectively.Additionally,ablation experiment results demonstrate that each structure is effective and can achieve the best result within the acceptable parameter range.
