XR-MSF-Unet：新冠肺炎肺部CT图像自动分割模型

doi:10.3778/j.issn.1673-9418.2203023

计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (8): 1850-1864.DOI: 10.3778/j.issn.1673-9418.2203023

XR-MSF-Unet：新冠肺炎肺部CT图像自动分割模型

谢娟英, 张凯云

陕西师范大学计算机科学学院，西安 710119

收稿日期:2022-02-14 修回日期:2022-03-31 出版日期:2022-08-01 发布日期:2022-08-19
作者简介:谢娟英（1971—），女，陕西西安人，博士，教授，博士生导师，CCF高级会员，主要研究方向为机器学习、数据挖掘、生物医学数据分析等。
张凯云（1995—），女，宁夏固原人，硕士研究生，CCF学生会员，主要研究方向为机器学习、生物医学数据分析。
基金资助:
国家自然科学基金(62076159);国家自然科学基金(12031010);国家自然科学基金(61673251);中央高校基本科研业务费专项资金(GK202105003);中央高校基本科研业务费专项资金(GK201701006);陕西师范大学研究生创新基金项目(2018TS078)

XR-MSF-Unet: Automatic Segmentation Model for COVID-19 Lung CT Images

XIE Juanying, ZHANG Kaiyun

School of Computer Science, Shaanxi Normal University, Xi’an 710119, China

Received:2022-02-14 Revised:2022-03-31 Online:2022-08-01 Published:2022-08-19
About author:XIE Juanying, born in 1971, Ph.D., professor, Ph.D. supervisor, senior member of CCF. Her research interests include machine learning, data mining, biomedical data analysis, etc.
ZHANG Kaiyun, born in 1995, M.S. candidate, student member of CCF. Her research interests include machine learning and biomedical data analysis.
Supported by:
the National Natural Science Foundation of China(62076159);the National Natural Science Foundation of China(12031010);the National Natural Science Foundation of China(61673251);the Fundamental Research Funds for the Central Universities of China(GK202105003);the Fundamental Research Funds for the Central Universities of China(GK201701006);the Graduate Innovation Fund of Shaanxi Normal University(2018TS078)

摘要/Abstract

摘要：

新冠肺炎给人类带来极大威胁，自动精确分割新冠肺炎CT图像感染区域可以辅助医生进行诊断治疗，但新冠肺炎的弥漫性感染、感染区域形状多变、与其他肺部组织极易混淆等给CT图像分割带来挑战。为此，提出新冠肺炎肺部CT图像分割新模型XR-MSF-Unet，采用XR卷积模块代替U-Net的两层卷积，XR各分支的不同卷积核使模型能够提取更多有用特征;提出即插即用的融合多尺度特征的注意力模块MSF，融合不同感受野、全局、局部和空间特征，强化网络的细节分割效果。在COVID-19 CT公开数据集的实验表明：提出的XR模块能够增强模型的特征提取能力，提出的MSF模块结合XR模块，能够有效提高模型对新冠肺炎感染区域的分割效果;提出的XR-MSF-Unet模型取得了很好的分割效果，其Dice、IOU、F1-Score和Sensitivity指标分别比基模型U-Net的相应指标高出3.21、5.96、1.22和4.83个百分点，且优于同类模型的分割效果，实现了新冠肺炎肺部CT图像的自动有效分割。

关键词: 新冠肺炎, CT图像分割, U-Net, XR模块, MSF模块

Abstract:

The COVID-19 epidemic has threatened the human being. The automatic and accurate segmentation for the infected area of the COVID-19 CT images can help doctors to make correct diagnosis and treatment in time. However, it is very challenging to achieve perfect segmentation due to the diffuse infections of the COVID-19 to the patient lungs and irregular shapes of the infected areas and very similar infected areas to other lung tissues. To tackle these challenges, the XR-MSF-Unet model is proposed in this paper for segmenting the COVID-19 lung CT images of patients. The XR (X ResNet) convolution module is proposed in this model to replace the two-layer convolution operations of U-Net, so as to extract more informative features for achieving good segmentation results by multiple branches of XR. The plug and play attention mechanism module MSF (multi-scale features fusion module) is proposed in XR-MSF-Unet to fuse multi-scale features from different scales of reception fields, global, local and spatial features of CT images, so as to strengthen the detail segmentation effect of the model. Extensive experiments on the public COVID-19 CT images demonstrate that the proposed XR module can strengthen the capability of the XR-MSF-Unet model to extract effective features, and the MSF module plus XR module can effectively improve the segmentation capability of the XR-MSF-Unet model for the infected areas of the COVID-19 lung CT images. The proposed XR-MSF-Unet model obtains good segmentation results. Its segmentation perfor-mance is superior to that of the original U-Net model by 3.21, 5.96, 1.22 and 4.83 percentage points in terms of Dice, IOU, F1-Score and Sensitivity, and it defeats other same type of models, realizing automatic segmentation to the COVID-19 lung CT images.

Key words: COVID-19, CT image segmentation, U-Net, XR module, MSF module

中图分类号:

TP181

谢娟英, 张凯云. XR-MSF-Unet：新冠肺炎肺部CT图像自动分割模型[J]. 计算机科学与探索, 2022, 16(8): 1850-1864.

XIE Juanying, ZHANG Kaiyun. XR-MSF-Unet: Automatic Segmentation Model for COVID-19 Lung CT Images[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(8): 1850-1864.

图/表 20

图1 XR-MSF-Unet网络结构示意图

Fig.1 Diagram of XR-MSF-Unet network structure

图2 XR模块结构图

Fig.2 Diagram of XR module structure

图3 多尺度特征融合的注意力模块MSF

Fig.3 MSF attention module fusing multi-scale features

图4 全局注意力模块GAM

Fig.4 Global attention module GAM

表1 实验用COVID-19 CT图像数据集

Table 1 COVID-19 CT image datasets for experiments

数据集	CT切片数量	COVID-19的 CT切片数	COVID-19 病例数
COVID-19-1	100	100	~60
COVID-19-2	829	373	9
COVID-19-3	1 844	1 844	20
COVID-19-4	785	785	50

表2 不同优化器下的模型性能比较

Table 2 Comparison of model performance using different optimizers

优化器	Dice	IOU	F1-Score	Sensitivity
RMSProp	0.578 5	0.414 7	0.567 6	0.551 6
Adam	0.566 5	0.388 6	0.532 5	0.609 2
SGD	0.211 0	0.159 4	0.236 6	0.418 9
Adamax	0.528 7	0.455 2	0.559 2	0.650 5

表3 数据扩增有效性测试结果

Table 3 Testing results of data augmentation efficacy

数据集	Dice	IOU	F1-Score	Sensitivity
无数据扩增	0.578 5	0.414 7	0.567 6	0.551 6
有数据扩增	0.614 4	0.417 3	0.623 6	0.621 9

表4 XR模块中残差块参数 X测试的实验结果

Table 4 Experimental results of parameter X of residual blocks embedded in XR module

残差块参数 $X$	Dice	IOU	F1-Score	Sensitivity
0	0.614 4	0.417 3	0.623 6	0.621 9
1	0.603 4	0.394 9	0.524 2	0.608 1
2	0.601 4	0.404 9	0.538 7	0.618 6
4	0.618 0	0.422 3	0.563 3	0.625 7
8	0.623 4	0.443 6	0.583 1	0.637 6
16	0.627 2	0.430 8	0.569 1	0.626 2
32	0.635 4	0.458 7	0.624 3	0.645 8
64	0.560 5	0.330 9	0.451 3	0.585 1

表4 XR模块中残差块参数 X测试的实验结果

Table 4 Experimental results of parameter X of residual blocks embedded in XR module

残差块参数 $X$	Dice	IOU	F1-Score	Sensitivity
0	0.614 4	0.417 3	0.623 6	0.621 9
1	0.603 4	0.394 9	0.524 2	0.608 1
2	0.601 4	0.404 9	0.538 7	0.618 6
4	0.618 0	0.422 3	0.563 3	0.625 7
8	0.623 4	0.443 6	0.583 1	0.637 6
16	0.627 2	0.430 8	0.569 1	0.626 2
32	0.635 4	0.458 7	0.624 3	0.645 8
64	0.560 5	0.330 9	0.451 3	0.585 1

表5 SAM模块的卷积核大小测试实验结果

Table 5 Experimental results for testing kernel sizes of SAM module

Kernel size	Dice	IOU	F1-Score	Sensitivity
1×1	0.624 3	0.448 3	0.611 5	0.648 6
3×3	0.600 9	0.398 6	0.529 5	0.613 6
5×5	0.600 8	0.409 4	0.538 8	0.623 8
7×7	0.567 9	0.339 1	0.460 6	0.590 3
9×9	0.610 4	0.416 1	0.555 3	0.625 0

表6 MSF模块特征融合的权重测试实验

Table 6 Experiments for testing weights for feature fusion of MSF module

权重值	Dice	IOU	F1-Score	Sensitivity
1∶1	0.624 3	0.448 3	0.611 5	0.648 6
2∶3	0.067 8	0.043 8	0.058 6	0.071 2
3∶2	0.195 0	0.198 6	0.132 7	0.113 4
3∶7	0.070 1	0.048 3	0.063 4	0.073 6
7∶3	0.263 5	0.144 9	0.256 9	0.292 4
1∶4	0.056 5	0.040 1	0.055 8	0.077 1
4∶1	0.219 5	0.135 9	0.277 4	0.233 3
1∶9	0.594 7	0.379 6	0.506 2	0.604 0
9∶1	0.606 3	0.390 8	0.520 5	0.607 5

图5 MSF模块在U-Net的不同位置

Fig.5 MSF modules in different locations of U-Net

表7 MSF模块在U-Net不同位置的模型性能

Table 7 Performance of U-Net embedding MSF module in different positions

MSF模块的位置	Dice	IOU	F1-Score	Sensitivity
L1	0.460 1	0.335 7	0.372 4	0.583 9
L2	0.624 3	0.448 3	0.611 5	0.648 6
L3	0.580 4	0.347 8	0.472 1	0.593 5
L4	0.593 9	0.407 4	0.541 9	0.620 1
L5	0.582 3	0.364 0	0.484 7	0.601 1
L6	0.600 7	0.412 7	0.547 5	0.617 3
L7	0.617 5	0.397 8	0.530 4	0.608 7
L8	0.609 7	0.413 1	0.546 5	0.619 6

表8 MSF注意力模块与其他注意力模块的性能对比

Table 8 Performance comparison of MSF and other attention modules

模型	Dice	IOU	F1-Score	Sensitivity
U-Net^[4]	0.614 4	0.417 3	0.623 6	0.621 9
SE^[29]+U-Net	0.551 5	0.307 6	0.423 2	0.579 2
CBAM^[30]+U-Net	0.592 9	0.362 0	0.492 3	0.598 6
SCSE^[31]+U-Net	0.615 2	0.409 5	0.545 7	0.616 7
ECA^[32]+U-Net	0.593 7	0.397 3	0.528 4	0.616 1
MSF+U-Net	0.624 3	0.448 3	0.611 5	0.648 6

图6 MSF模块与其他注意力模块的细节分割对比

Fig.6 Comparison of segmentation details between MSF module and other attention modules

图7 MSF模块嵌入不同Baseline的实验结果

Fig.7 Experimental results of MSF module embedded in different baselines

表9 MSF模块的消融实验及复杂度分析

Table 9 Ablation experiments and complexity analysis of MSF module

Baseline	组件				评价指标
Baseline	DRF	GAM	LAM	SAM	Dice	IOU	F1-Score	Sensitivity	#Parameters/10⁶	FPS
U-Net					0.614 4	0.417 3	0.623 6	0.621 9	16.47	43
		√	√	√	0.270 1	0.148 7	0.264 3	0.373 5	48.76	16
	√		√	√	0.536 4	0.401 8	0.590 3	0.602 6	52.52	18
	√	√		√	0.607 6	0.416 2	0.553 1	0.627 8	55.66	9
	√	√	√		0.132 0	0.123 4	0.231 9	0.323 9	56.69	16
	√	√	√	√	0.624 3	0.448 3	0.611 5	0.648 6	64.54	30

表10 不同模块对U-Net模型性能影响的消融实验

Table 10 Ablation experimental results of different modules on performance of U-Net

模型	Dice	IOU	F1-Score	Sensitivity
U-Net	0.614 4	0.417 3	0.623 6	0.621 9
XR+U-Net	0.635 4	0.458 7	0.624 3	0.645 8
MSF+U-Net	0.624 3	0.448 3	0.611 5	0.648 6
XR-MSF-Unet	0.646 5	0.476 9	0.635 8	0.670 2

表11 本文XR-MSF-Unet与其他方法的性能比较

Table 11 Performance comparison of XR-MSF-Unet and other methods

模型	Dice	IOU	F1-Score	Sensitivity	#Parameters/10⁶	FPS
U-Net^[4]	0.614 4	0.417 3	0.623 6	0.621 9	16.47	45
Attention U-Net^[40]	0.605 2	0.391 3	0.520 3	0.539 6	33.26	46
UNet++^[13]	0.551 3	0.317 2	0.432 3	0.585 6	34.93	26
FusionNet^[38]	0.595 9	0.378 7	0.506 2	0.606 7	77.88	19
SegNet^[39]	0.546 1	0.303 1	0.419 5	0.580 5	28.08	62
FCN^[23]	0.601 7	0.402 3	0.559 5	0.618 1	19.17	66
PraNet^[41]	0.539 1	0.310 3	0.521 0	0.597 3	31.04	41
BASNet^[42]	0.634 2	0.461 7	0.611 5	0.639 6	83.03	19
CaraNet^[43]	0.613 2	0.445 7	0.591 8	0.603 5	44.48	40
UNeXt^[44]	0.624 2	0.451 6	0.614 5	0.645 8	24.56	41
XR-MSF-Unet	0.646 5	0.476 9	0.635 8	0.670 2	98.21	15

图8 不同方法的分割效果可视化展示

Fig.8 Visualization of segmentation results of different methods

表12 XR-MSF-Unet模型的泛化性能测试

Table 12 Generalization test of XR-MSF-Unet model

数据集	Dice	IOU	F1-Score	Sensitivity
COVID-19-1	0.646 5	0.476 9	0.635 8	0.670 2
COVID-19-2	0.795 0	0.821 7	0.845 3	0.856 4
COVID-19-3	0.881 6	0.771 5	0.860 1	0.875 2
COVID-19-4	0.727 8	0.761 1	0.771 5	0.806 9

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XR-MSF-Unet：新冠肺炎肺部CT图像自动分割模型

XR-MSF-Unet: Automatic Segmentation Model for COVID-19 Lung CT Images

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