基于一致性学习的多尺度双注意力网络的用于各种肿瘤尺寸的肺肿瘤自动分割。

Automated lung tumor segmentation robust to various tumor sizes using a consistency learning-based multi-scale dual-attention network.

机构信息

Department of Software Convergence, Seoul Women's University, Seoul, Republic of Korea.

R&D Center, Boryung Ltd., Seoul, Republic of Korea.

出版信息

J Xray Sci Technol. 2023;31(5):879-892. doi: 10.3233/XST-230003.

DOI:10.3233/XST-230003

PMID:37424487

Abstract

BACKGROUND

It is often difficult to automatically segment lung tumors due to the large tumor size variation ranging from less than 1 cm to greater than 7 cm depending on the T-stage.

OBJECTIVE

This study aims to accurately segment lung tumors of various sizes using a consistency learning-based multi-scale dual-attention network (CL-MSDA-Net).

METHODS

To avoid under- and over-segmentation caused by different ratios of lung tumors and surrounding structures in the input patch according to the size of the lung tumor, a size-invariant patch is generated by normalizing the ratio to the average size of the lung tumors used for the training. Two input patches, a size-invariant patch and size-variant patch are trained on a consistency learning-based network consisting of dual branches that share weights to generate a similar output for each branch with consistency loss. The network of each branch has a multi-scale dual-attention module that learns image features of different scales and uses channel and spatial attention to enhance the scale-attention ability to segment lung tumors of different sizes.

RESULTS

In experiments with hospital datasets, CL-MSDA-Net showed an F1-score of 80.49%, recall of 79.06%, and precision of 86.78%. This resulted in 3.91%, 3.38%, and 2.95% higher F1-scores than the results of U-Net, U-Net with a multi-scale module, and U-Net with a multi-scale dual-attention module, respectively. In experiments with the NSCLC-Radiomics datasets, CL-MSDA-Net showed an F1-score of 71.7%, recall of 68.24%, and precision of 79.33%. This resulted in 3.66%, 3.38%, and 3.13% higher F1-scores than the results of U-Net, U-Net with a multi-scale module, and U-Net with a multi-scale dual-attention module, respectively.

CONCLUSIONS

CL-MSDA-Net improves the segmentation performance on average for tumors of all sizes with significant improvements especially for small sized tumors.

摘要

背景

由于肿瘤大小变化范围较大，从小于 1cm 到大于 7cm 不等，取决于 T 分期，因此自动分割肺肿瘤通常较为困难。

目的

本研究旨在使用基于一致性学习的多尺度双注意力网络（CL-MSDA-Net）准确分割各种大小的肺肿瘤。

方法

为避免根据肺肿瘤大小输入斑块中肺肿瘤和周围结构的比例造成的欠分割和过分割，通过归一化比例到用于训练的肺肿瘤的平均大小来生成大小不变的斑块。大小不变斑块和大小变化斑块在基于一致性学习的网络上进行训练，该网络由两个共享权重的分支组成，通过一致性损失为每个分支生成相似的输出。每个分支的网络都有多尺度双注意力模块，该模块学习不同尺度的图像特征，并使用通道和空间注意力增强不同大小肺肿瘤的尺度注意力能力。

结果

在医院数据集的实验中，CL-MSDA-Net 的 F1 得分为 80.49%，召回率为 79.06%，准确率为 86.78%。这比 U-Net、具有多尺度模块的 U-Net 和具有多尺度双注意力模块的 U-Net 的结果分别高出 3.91%、3.38%和 2.95%。在 NSCLC-Radiomics 数据集的实验中，CL-MSDA-Net 的 F1 得分为 71.7%，召回率为 68.24%，准确率为 79.33%。这比 U-Net、具有多尺度模块的 U-Net 和具有多尺度双注意力模块的 U-Net 的结果分别高出 3.66%、3.38%和 3.13%。