基于阶梯结构的U-Net结肠息肉分割算法

时永刚; 李祎; 周治国; 张岳; 夏卓岩

doi:10.11999/JEIT210916

基于阶梯结构的U-Net结肠息肉分割算法

doi: 10.11999/JEIT210916

北京理工大学信息与电子学院北京 100081

基金项目: 国家自然科学基金(60971133, 61271112)

详细信息

作者简介:
时永刚：男，1969年生，副教授，研究方向为医学图像分割、目标检测识别、目标分类、图像复原和超分辨率重建

李祎：女，1996年生，硕士生，研究方向为医学图像分割

周治国：男，1977年生，副教授，研究方向为智能感知与导航

张岳：男，1996年生，硕士生，研究方向为医学图像处理、深度学习

夏卓岩：男，1997年生，硕士生，研究方向为图像分割、目标检测与分类、目标识别

通讯作者:
时永刚　ygshi@bit.edu.cn

中图分类号: TN911.73; R735.34
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出版历程
- 收稿日期: 2021-09-01
- 修回日期: 2021-12-21
- 录用日期: 2021-12-21
- 网络出版日期: 2021-12-27
- 刊出日期: 2022-01-10

Polyp Segmentation Using Stair-structured U-Net

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Funds: The National Natural Science Foundation of China (60971133, 61271112)

摘要

摘要: 结肠息肉的精确分割对结直肠癌的诊断和治疗具有重要意义，目前的分割方法普遍存在有伪影、分割精度低等问题。该文提出一种基于阶梯结构的U-Net结肠息肉分割算法(SU-Net)，使用U-Net的U型结构，利用Kronecker乘积来扩展标准空洞卷积核，构成Kronecker空洞卷积下采样有效扩大感受野，弥补传统空洞卷积容易丢失的细节特征；应用具有阶梯结构的融合模块，遵循扩展和堆叠原则形成阶梯状的分层结构，有效捕获上下文信息并从多个尺度聚合特征；在解码器引入卷积重构上采样模块生成密集的像素级预测图，捕获双线性插值上采样中缺少的精细信息。在Kvasir-SEG数据集和CVC-EndoSceneStill数据集上对模型进行了测试，相似系数(Dice)指标和交并比(IoU)指标分别达到了87.51%, 88.75%和82.30%, 85.64%。实验结果表明，该文所提方法改善了因过度曝光、低对比度引起的分割精度低的问题，同时消除了边界外部的图像伪影和图像内部不连贯的现象，优于其他息肉分割方法。
- 图像分割 /
- 结肠息肉图像 /
- 空洞卷积 /
- U-Net
Abstract: The precise segmentation of colon polyps plays a significant role in the diagnosis and treatment of colorectal cancer. The existing segmentation methods have generally artifacts and low segmentation accuracy. In this paper, Stair-structured U-Net (SU-Net) is proposed to segment polyp, using U-shaped structure. The Kronecker product is used to extend the standard atrous convolution kernel to keep more detail structrural features that are easily ignored. Stair-structured fusion module is applied to encompass effectively multi-scale features. The decoder introduces a convolutional reshaped upsampling module to generate pixel-level predictions. Experiments are performed on the Kvasir-SEG dataset and the CVC-EndoSceneStill dataset. The results show that the method proposed in this paper outperforms other polyp segmentation methods in Dice and Intersection-over-Union(IoU).
- Image segmentation /
- Colorectal polyp image /
- Atrous convolution /
- U-Net

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图 1 SU-Net整体框架

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图 2 不同类型卷积核和KACD模块

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图 3 阶梯结构的融合模块

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图 4 卷积重构上采样模块

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图 5 SU-Net与其他分割模型在EndoSceneStill数据集上的分割结果

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图 6 SU-Net与其他分割模型在Kvasir-SEG数据集上的分割结果

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表 1 SU-Net消融实验列表

序号	实验描述
1	基线
2	仅将基线里的空洞卷积替换为Kronecker空洞卷积
3	将实验2中下采样替换为Kronecker空洞卷积下采样
4	在实验3中的编码器解码器之间加入阶梯结构的融合模块
5	SU-Net

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表 2 在EndoSceneStill数据集上各实验的量化结果

评估标准	消融实验编号
评估标准	1	2	3	4	5
召回率	0.7819	0.8195	0.8028	0.8027	0.8237
特异性	0.9931	0.9908	0.9946	0.9947	0.9929
精确率	0.9185	0.8747	0.9179	0.9119	0.9007
F₁	0.7899	0.7994	0.8088	0.8174	0.8230
F₂	0.7791	0.8025	0.7980	0.8046	0.8175
IoU	0.7194	0.7214	0.7360	0.7450	0.7499
IoU_B	0.9601	0.9599	0.9269	0.9627	0.9630
IoU_M	0.8397	0.8407	0.8494	0.8538	0.8564
Dice	0.7899	0.7994	0.8088	0.8174	0.8230

下载: 导出CSV

表 3 在Kvasir-SEG数据集上各实验的量化结果

评估标准	消融实验编号
评估标准	1	2	3	4	5
召回率	0.8664	0.8631	0.8636	0.8750	0.8752
特异性	0.9840	0.9854	0.9844	0.9858	0.9866
精确率	0.8921	0.9006	0.9163	0.9021	0.9207
F₁	0.8560	0.8607	0.8654	0.8689	0.8751
F₂	0.8574	0.8673	0.8602	0.8681	0.8718
IoU	0.7866	0.7920	0.7957	0.8032	0.8173
IoU_B	0.9534	0.9539	0.9520	0.9532	0.9577
IoU_M	0.8700	0.8730	0.8738	0.8782	0.8875
Dice	0.8560	0.8607	0.8654	0.8689	0.8751

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表 4 不同模型在EndoSceneStill数据集中的量化评估结果

模型	召回率	特异性	精确率	F₁	F₂	IoU	IoU_B	IoU_M	Dice
U-Net	0.6839	0.9954	0.9222	0.7113	0.6910	0.6314	0.9515	0.7914	0.7113
Attention unet	0.6744	0.9962	0.9373	0.7084	0.6833	0.6260	0.9504	0.7882	0.7084
TKCN	0.8110	0.9866	0.8565	0.7819	0.7875	0.7023	0.9536	0.8280	0.7819
Xception	0.8017	0.9920	0.8964	0.7940	0.7906	0.7220	0.9575	0.8398	0.7940
DeepLabV3+	0.7611	0.9919	0.8543	0.7542	0.7505	0.6833	0.9545	0.8189	0.7542
PraNet	0.7973	0.9937	0.9215	0.8016	0.7945	0.7349	0.9610	0.8480	0.8016
SU-Net	0.8237	0.9929	0.9007	0.8230	0.8175	0.7499	0.9630	0.8564	0.8230

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表 5 不同模型在Kvasir-SEG数据集中的量化评估结果

模型	召回率	特异性	精确率	F₁	F₂	IoU	IoU_B	IoU_M	Dice
U-Net	0.8408	0.9707	0.8315	0.8017	0.8161	0.7099	0.9331	0.8215	0.8017
Attention unet	0.8576	0.9682	0.8317	0.8105	0.8283	0.7249	0.9340	0.8294	0.8105
TKCN	0.8651	0.9826	0.8989	0.8552	0.8567	0.7811	0.9473	0.8642	0.8552
Xception	0.8702	0.9831	0.9041	0.8662	0.8639	0.7982	0.9504	0.8743	0.8662
DeepLabV3+	0.8879	0.9812	0.8938	0.8725	0.8770	0.8110	0.9550	0.8830	0.8725
PraNet	0.8763	0.9859	0.9154	0.8743	0.8718	0.8110	0.9557	0.8833	0.8743
SU-Net	0.8752	0.9866	0.9207	0.8751	0.8718	0.8173	0.9577	0.8875	0.8751

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参考文献(22)

[1]	GSCHWANTLER M, KRIWANEK S, LANGNER E, et al. High-grade dysplasia and invasive carcinoma in colorectal adenomas: A multivariate analysis of the impact of adenoma and patient characteristics[J]. European Journal of Gastroenterology & Hepatology, 2002, 14(2): 183–188. doi: 10.1097/00042737-200202000-00013
[2]	ARNOLD M, SIERRA M S, LAVERSANNE M, et al. Global patterns and trends in colorectal cancer incidence and mortality[J]. Gut, 2017, 66(4): 683–691. doi: 10.1136/gutjnl-2015-310912
[3]	PUYAL J G B, BHATIA K K, BRANDAO P, et al. Endoscopic polyp segmentation using a hybrid 2D/3D CNN[C]. 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru, 2020: 295–305.
[4]	TASHK A, HERP J, and NADIMI E. Fully automatic polyp detection based on a novel u-net architecture and morphological post-process[C]. 2019 IEEE International Conference on Control, Artificial Intelligence, Robotics & Optimization, Athens, Greece, 2019: 37–41.
[5]	WANG Pu, XIAO Xiao, BROWN J R G, et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy[J]. Nature Biomedical Engineering, 2018, 2(10): 741–748. doi: 10.1038/s41551-018-0301-3
[6]	SORNAPUDI S, MENG F, and YI S. Region-based automated localization of colonoscopy and wireless capsule endoscopy polyps[J]. Applied Sciences, 2019, 9(12): 2404. doi: 10.3390/app9122404
[7]	FAN Dengping, JI Gepeng, ZHOU Tao, et al. PraNet: Parallel reverse attention network for polyp segmentation[C]. 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru, 2020: 263–273.
[8]	FENG Ruiwei, LEI Biwen, WANG Wenzhe, et al. SSN: A stair-shape network for real-time polyp segmentation in colonoscopy images[C]. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, USA, 2020: 225–229.
[9]	JI Gepeng, CHOU Yucheng, FAN Dengping, et al. Progressively normalized self-attention network for video polyp segmentation[J]. arXiv: 2105.08468, 2021.
[10]	LIN Ailiang, CHEN Bingzhi, XU Jiayu, et al. DS-TransUNet: Dual swin transformer U-Net for medical image segmentation[J]. arXiv: 2106.06716, 2021.
[11]	ZHANG Yundong, LIU Huiye, and HU Qiang. TransFuse: Fusing transformers and CNNs for medical image segmentation[J]. arXiv: 2102.08005, 2021.
[12]	JHA D, SMEDSRUD P H, RIEGLER M A, et al. Kvasir-SEG: A segmented polyp dataset[C]. 26th International Conference on Multimedia Modeling, Daejeon, Korea, 2020: 451–462.
[13]	VÁZQUEZ D, BERNAL J, SÁNCHEZ F J, et al. A benchmark for endoluminal scene segmentation of colonoscopy images[J]. Journal of Healthcare Engineering, 2017, 2017: 4037190. doi: 10.1155/2017/4037190
[14]	RONNEBERGER O, FISCHER P, and BROX T. U-Net: Convolutional networks for biomedical image segmentation[C]. 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015: 234–241.
[15]	LONG J, SHELHAMER E, and DARRELL T. Fully convolutional networks for semantic segmentation[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA, 2015: 3431–3440.
[16]	WU Tianyi, TANG Sheng, ZHANG Rui, et al. Tree-structured kronecker convolutional network for semantic segmentation[C]. 2019 IEEE International Conference on Multimedia and Expo (ICME), Shanghai, China, 2019: 940–945.
[17]	CHOLLET F. Xception: Deep learning with depthwise separable convolutions[C]. The 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2017: 1800–1807.
[18]	HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. The 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016: 770–778.
[19]	SHI Wenzhe, CABALLERO J, HUSZÁR F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]. The 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016: 1874–1883.
[20]	KINGMA D P and BA J. Adam: A method for stochastic optimization[J]. arXiv: 1412.6980, 2017.
[21]	OKTAY O, SCHLEMPER J, LE FOLGOC L, et al. Attention U-Net: Learning where to look for the pancreas[J]. arXiv: 1804.03999v3, 2018.
[22]	CHEN L C, ZHU Yukun, PAPANDREOU G, et al. Encoder-decoder with Atrous separable convolution for semantic image segmentation[C]. The 15th European Conference on Computer Vision (ECCV), Munich, Germany, 2018: 833–851.