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

doi:10.3778/j.issn.1673-9418.2203023

Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (8): 1850-1864.DOI: 10.3778/j.issn.1673-9418.2203023

• Graphics and Image • Previous Articles Next Articles

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)

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

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)

Abstract

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

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

CLC Number:

TP181

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.

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.

Figures/Tables 20

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

Fig.2 Diagram of XR module structure

Fig.3 MSF attention module fusing multi-scale features

Fig.4 Global attention module GAM

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Fig.8 Visualization of segmentation results of different methods

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

References 45

[1]	新型冠状病毒感染的肺炎诊疗方案(试行第五版)[EB/OL]. ( 2020-02-04)[2022-01-31]. http://www.nhc.gov.cn/yzygj/s7653p/202002/3b09b894ac9b4204a79db5b8912d4440/files/7260301a393845fc87fcf6dd52965ecb.pdf.
[2]	刘辰, 肖志勇, 杜年茂. 改进的卷积神经网络在医学图像分割上的应用[J]. 计算机科学与探索, 2019, 13(9): 1593-1603.
	LIU C, XIAO Z Y, DU N M. Application of improved con-volutional neural network in medical image segmentation[J]. Journal of Frontiers of Computer Science and Techno-logy, 2019, 13(9): 1593-1603.
[3]	胡敏, 周秀东, 黄宏程, 等. 基于改进U型神经网络的脑出血CT图像分割[J]. 电子与信息学报, 2022, 44(1): 11-17.
	HU M, ZHOU X D, HUANG H C, et al. Computed-tomo-graphy image segmentation of cerebral hemorrhage based on improved U-shaped neural network[J]. Journal of Elec-tronics & Information Technology, 2022, 44(1): 11-17.
[4]	RONNEBERGER O, FISCHER P, BROX T. U-Net: convo-lutional networks for biomedical image segmentation[J]. Medical Image Computing and Computer-Assisted Interven-tion, 2015, 9351: 234-241.
[5]	沈怀艳, 吴云. 基于MSFA-Net的肝脏CT图像分割方法[J/OL]. 计算机科学与探索( 2021-08-03) [2022-01-31].http://kns.cnki.net/kcms/detail/11.5602.TP.20210803.1652.004.html.
	SHEN H Y, WU Y. Liver CT image segmentation method based on MSFA-Net[J/OL]. Journal of Frontiers of Computer Science and Technology(2021-08-03) [2022-01-31]. http://kns.cnki.net/kcms/detail/11.5602.TP.20210803.1652.004.html.
[6]	余昇, 王康健, 何灵敏, 等. 基于改进U-net网络的气胸分割方法[J]. 计算机工程与应用, 2022, 58(3): 207-214.
	YU S, WANG K J, HE L M, et al. Pneumothorax segmenta-tion method based on improved U-net network[J]. Computer Engineering and Applications, 2022, 58(3): 207-214.
[7]	钱宝鑫, 肖志勇, 宋威. 改进的卷积神经网络在肺部图像上的分割应用[J]. 计算机科学与探索, 2020, 14(8): 1358-1367.
	QIAN B X, XIAO Z Y, SONG W. Application of improved convolutional neural network in lung image segmentation[J]. Journal of Frontiers of Computer Science and Techno-logy, 2020, 14(8): 1358-1367.
[8]	姬广海, 黄满华, 张庆, 等. 新型冠状病毒肺炎CT表现及动态变化[J]. 中国医学影像技术, 2020, 36(2): 242-247.
	JI G H, HUANG M H, ZHANG Q, et al. CT manifestations and dynamic changes of corona virus disease 2019[J]. Chin-ese Journal of Medical Imaging Technology, 2020, 36(2): 242-247.
[9]	许玉环, 吕晓艳, 张见增, 等. 新型冠状病毒肺炎不同临床分型的CT特征[J]. 中国医学影像学杂志, 2020, 28(12): 887-890.
	XU Y H, LV X Y, ZHANG J Z, et al. CT features of different clinical types of COVID-19[J]. Chinese Journal of Medical Imaging, 2020, 28(12): 887-890.
[10]	WANG Z, LIU Q D, DOU Q, et al. Contrastive cross-site learning with redesigned net for COVID-19 CT classifica-tion[J]. IEEE Journal of Biomedical and Health Informa-tics, 2020, 24(10): 2806-2813.
[11]	BUTT C, GILL J L, CHUN D, et al. Deep learning system to screen coronavirus disease 2019 pneumonia[J]. Applied Intelligence, 2020(5): 1-7.
[12]	CHEN J, WU L L, ZHANG J, et al. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography[J]. Scientific Reports, 2020, 10(1): 191-196.
[13]	ZHOU Z W, SIDDIQUUE M M R, TAJBAKHSH N, et al. UNet++: a nested U-Net architecture for medical image seg-mentation[C]// LNCS 11045: Proceedings of the 2018 Inter-national Workshop on Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Cham: Springer, 2018: 3-11.
[14]	SHAN F, GAO Y Z, WANG J, et al. Abnormal lung quan-tification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction[J]. Medical Physics, 2021, 48(4): 1633-1645.
[15]	CHEN X C, YAO L N, ZHANG Y, et al. Residual attention U-Net for automated multi-class segmentation of COVID-19 chest CT images[J]. arXiv:2004.05645, 2020.
[16]	FAN D P, ZHOU T, JI G P, et al. Inf-Net: automatic COVID-19 lung infection segmentation from CT images[J]. IEEE Transactions on Medical Imaging, 2020, 39: 2626-2637.
[17]	BUDAK Ü, ÇIBUK M, CÖMERT Z, et al. Efficient COVID-19 segmentation from CT slices exploiting semantic segmen-tation with integrated attention mechanism[J]. Journal of Digital Imaging, 2021, 34(2): 263-272.
[18]	KUMAR SINGH V, ABDEL-NASSER M, PANDEY N, et al. LungINFseg: segmenting COVID-19 infected regions in lung CT images based on a receptive-field-aware deep lear-ning framework[J]. Diagnostics, 2021, 11(2): 158.
[19]	WANG X, ZHENG C S, DENG X B, et al. A weakly-super-vised framework for COVID-19 classification and lesion lo-calization from chest CT[J]. IEEE Transactions on Medical Imaging, 2020, 39(8): 2615-2625.
[20]	吴辰文, 梁雨欣, 田鸿雁. 改进卷积神经网络的COVID-19 CT影像分类方法研究[J]. 计算机工程与应用, 2022, 58(2): 225-234.
	WU C W, LIANG Y X, TIAN H Y. Research on COVID-19 CT image classification method based on improved convo-lutional neural network[J]. Computer Engineering and App-lications, 2022, 58(2): 225-234.
[21]	XIE S N, GIRSHICK R B, DOLLÁR P, et al. Aggregated residual transformations for deep neural networks[C]// Pro-ceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Was-hington: IEEE Computer Society, 2017: 5987-5995.
[22]	姜峰, 顾庆, 郝慧珍, 等. 基于内容的图像分割方法综述[J]. 软件学报, 2017, 28(1): 160-183.
	JIANG F, GU Q, HAO H Z, et al. Survey on content-based image segmentation methods[J]. Journal of Software, 2017, 28(1): 160-183.
[23]	LONG J, SHELHAMER E, DARRELL T. Fully convolu-tional networks for semantic segmentation[J]. IEEE Tran-sactions on Pattern Analysis and Machine Intelligence, 2015, 39(4): 640-651.
[24]	ALEX K, SUTSKEVER I, HINTON G E, et al. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2012, 60: 84-90.
[25]	SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, Jun 7-12, 2015. Washington: IEEE Computer Society, 2015: 1-9.
[26]	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recogni-tion, Las Vegas, Jun 27-30, 2016. Washington: IEEE Computer Society, 2016: 770-778.
[27]	CHAUDHARI S, POLATKAN G, RAMANATH R, et al. An attentive survey of attention models[J].arXiv:1904.02874, 2019.
[28]	TSOTSOS J K, CULHANE S M, WAI W Y K, et al. Mo-deling visual attention via selective tuning[J]. Artificial Inte-lligence, 1995, 78(1/2): 507-545.
[29]	HU J, SHEN L, ALBANIE S, et al. Squeeze-and-excitation networks[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, Jun 18-23, 2018. Washington: IEEE Computer Society, 2018: 7132-7141.
[30]	WOO S, PARK J, LEE J, et al. CBAM: convolutional block attention module[C]// LNCS 11211: Proceedings of the 15th European Conference on Computer Vision, Munich, Sep 8-14, 2018. Cham: Springer, 2018: 3-19.
[31]	ROY A G, NAVAB N, WACHINGER C. Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks[C]// LNCS 11070: Proceedings of the 21st Interna-tional Conference on Medical Image Computing and Com-puter Assisted Intervention, Granada, Sep 16-20, 2018. Cham: Springer, 2018: 421-429.
[32]	WANG Q, WU B, ZHU P, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]// Pro-ceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, Jun 13-19, 2020.Piscataway: IEEE, 2020: 11531-11539.
[33]	MILLETARI F, NAVAB N, AHMADI S A. V-Net: fully convolutional neural networks for volumetric medical image segmentation[C]// Proceedings of the 4th International Con-ference on 3D Vision, Stanford, Oct 25-28, 2016. Washing-ton: IEEE Computer Society, 2016: 565-571.
[34]	JENSSEN H B. COVID-19 CT segmentation dataset[EB/OL]. [2022-01-31]. https://medicalsegmentation.com/covid19/.
[35]	MA J, WANG Y, AN X, et al. Toward data efficient lear-ning: a benchmark for COVID-19 CT lung and infection seg-mentation[J]. Medical Physics, 2021, 48(3): 1197-1210.
[36]	MOROZOV S, ANDREYCHENKO A, PAVLOV N, et al. Mosmeddata: chest CT scans with COVID-19 related findings dataset[J]. arXiv:20050.6465, 2020.
[37]	CANNY J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 1986, 8(6): 679-698.
[38]	QUAN T M, HILDEBRAND D, JEONG W K. FusionNet: a deep fully residual convolutional neural network for image segmentation in connectomics[J]. Frontiers in Computer Science, 2021, 3: 1197-1210.
[39]	BADRINARAYANAN V, KENDALL A, CIPOLLA R. Seg-Net: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analy-sis and Machine Intelligence, 2017, 39: 2481-2495.
[40]	OKTAY O, SCHLEMPER J, FOLGOC L L, et al. Attention U-Net: learning where to look for the pancreas[J]. arXiv:1804.0399, 2018.
[41]	FAN D P, JI G P, ZHOU T, et al. PraNet: parallel reverse attention network for polyp segmentation[C]// LNCS 12266: Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, Lima, Oct 4-8, 2020. Cham: Springer, 2020: 263-273.
[42]	QIN X, FAN D, HUANG C, et al. Boundary-aware segmen-tation network for mobile and Web applications[J]. arXiv:2101.04704, 2021.
[43]	LOU A, GUAN S, LOEW M, et al. CaraNet: context axial reverse attention network for segmentation of small medical objects[J]. arXiv:2108.07368, 2021.
[44]	JEYA M J V, VISHAL M P. UNeXt: MLP-based rapid medi-cal image segmentation network[J]. arXiv:2203.04967, 2022.
[45]	ISENSEE F, JAEGER P F, KOHL S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nature Methods, 2021, 18: 203-211.

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

XR-MSF-Unet：新冠肺炎肺部CT图像自动分割模型

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 20

References 45

Related Articles 4

Recommended Articles

Metrics

[1]	LIU Lamei, WANG Xiaona, LIU Wanjun, QU Haicheng. Image Semantic Segmentation Method with Fusion of Transposed Convolution and Deep Residual [J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(9): 2132-2142.
[2]	ZHU Bingyu, LIU Zhen, ZHANG Jingxiang. COVID-19 Detection Algorithm Combining Grad-CAM and Convolutional Neural Network [J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(9): 2108-2120.
[3]	GU Penghui, XIAO Zhiyong. Application of Improved U-Net in Retinal Vessel Segmentation [J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(3): 683-691.
[4]	SONG Shujie, CUI Zhenchao, CHEN Liping, CHEN Xiangyang. Fundus Vessel Segmentation Algorithm Based on Multi-feature Fusion Neural Network [J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(12): 2401-2412.