SOSNet:一种非对称编码器-解码器结构的非小细胞肺癌CT图像分割模型

谢娟英; 张凯云

doi:10.12263/DZXB.20220853

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SOSNet:一种非对称编码器-解码器结构的非小细胞肺癌CT图像分割模型

学术论文 | 更新时间：2025-12-11

- SOSNet:一种非对称编码器-解码器结构的非小细胞肺癌CT图像分割模型
- SOSNet: An Asymmetric Encoder-Decoder Structure Model for Automatic Segmenting Non-Small Cell Lung Cancer CT Images
- 电子学报 2024年52卷第3期页码：824-837
- 作者机构：
  
  陕西师范大学计算机科学学院，陕西西安 710119
- 作者简介：
  
  [ "谢娟英女，1971年4月生于陕西省西安市.现为陕西师范大学计算机科学学院教授、博士生导师.获陕西省自然科学二等奖，《中国科学：信息科学》热点论文奖，中国科技期刊卓越行动计划优秀审稿人，入选领跑者F5000、ESI前1%高被引论文等.主要研究方向为机器学习、数据挖掘、生物医学数据分析等.在国内外发表学术论文80余篇.E-mail: xiejuany@snnu.edu.cn" ]
  [ "张凯云女，1995年10月生于宁夏回族自治区固原市.陕西师范大学计算机科学学院硕士研究生.主要研究方向为机器学习、生物医学数据分析. E-mail: kaiy.zhang@qq.com" ]
- 基金信息：
  
  国家自然科学基金(62076159;12031010;61673251);中央高校基本科研业务费项目(GK202105003)
- DOI：10.12263/DZXB.20220853
  中图分类号： TP181;TP391
- 收稿：2022-07-18，
  
  修回：2023-03-13，
  
  纸质出版：2024-03-25
- 稿件说明：
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谢娟英,张凯云.SOSNet:一种非对称编码器-解码器结构的非小细胞肺癌CT图像分割模型[J].电子学报,2024,52(03):824-837.

XIE Juan-ying, ZHANG Kai-yun.SOSNet: An Asymmetric Encoder-Decoder Structure Model for Automatic Segmenting Non-Small Cell Lung Cancer CT Images[J].Acta Electronica Sinica, 2024, 52(03): 824-837.
谢娟英,张凯云.SOSNet:一种非对称编码器-解码器结构的非小细胞肺癌CT图像分割模型[J].电子学报,2024,52(03):824-837. DOI：10.12263/DZXB.20220853

XIE Juan-ying, ZHANG Kai-yun.SOSNet: An Asymmetric Encoder-Decoder Structure Model for Automatic Segmenting Non-Small Cell Lung Cancer CT Images[J].Acta Electronica Sinica, 2024, 52(03): 824-837. DOI：10.12263/DZXB.20220853

摘要

非小细胞肺癌严重损害人类健康，早期非小细胞肺癌CT（Computed Tomography）图像中的肿瘤结节体积小，不易发现，极易造成漏诊和误诊.为了精确分割非小细胞肺癌CT图像中的小体积肿瘤结节，本文提出SOSNet（Small Object Segmentation Networks）自动分割模型，利用ResNet（Residual Network）基础层和空洞卷积构造非对称编码器-解码器结构作为分割主网络，利用轴向取反注意力模块ARA（Axial Reverse Attention）逐步擦除背景中对分割有影响的结构，再使用结构细化模块SR（Structure Refinement）对主网络输出的粗略特征图进行结构细化，从而实现非小细胞肺癌肿瘤结节分割.在非小细胞肺癌公开数据集的实验测试表明，本文提出的小目标自动分割模型SOSNet可以有效分割出非小细胞肺癌CT图像中的小体积肿瘤结节，其mDice（mean- Dice）、mIoU（mean Intersection over Union）、Sensitivity、F1、Specificity、平均绝对误差MAE（Mean Absolute Error）均优于当前最先进的小目标分割模型CaraNet（Context Axial Reverse Attention Network）.

Abstract

Non-small cell lung cancer (NSCLC) will imperil human health seriously. The tumor nodules at the early stage of NSCLC are so small that it is very difficult to detect them in the CT (Computed Tomography) images

which will easily lead to the missed diagnosis and misdiagnosis of NSCLS. To automatically segment the small tumor nodules in CT images of NSCLC accurately

the SOSNet (Small Object Segmentation Networks) model is proposed. The ResNet (Residual Network) base layer and the dilated convolution are adopted to construct the asymmetric encoder-decoder structure to be the segmentation main network of SOSNet. The ARA (Axial Reverse Attention) module is adopted to gradually erase those structures which may influence the segmentation results from the background. Then the SR (Structure Refinement) module is used to refine the rough feature maps outputted by the main network

so as to achieve the segmentation for NSCLC tumor nodules. Experimental results on the open access NSCLC datasets demonstrate that the proposed SOSNet model can effectively segment small volume tumor nodules in CT images of NSCLC. It is superior to the state-of-the-art small object segmentation model of CaraNet in terms of mDice (mean Dice)

mIoU (mean Intersection over Union)

Sensitivity

Specificity and MAE (Mean Absolute Error)

respectively.

关键词

Keywords

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