基于階梯結(jié)構(gòu)的U-Net結(jié)腸息肉分割算法

時(shí)永剛; 李祎; 周治國; 張?jiān)?/>
	<meta name=

doi:10.11999/JEIT210916

基于階梯結(jié)構(gòu)的U-Net結(jié)腸息肉分割算法

doi: 10.11999/JEIT210916

夏卓巖

北京理工大學(xué)信息與電子學(xué)院北京 100081

基金項(xiàng)目: 國家自然科學(xué)基金(60971133, 61271112)

詳細(xì)信息

作者簡(jiǎn)介:
時(shí)永剛：男，1969年生，副教授，研究方向?yàn)獒t(yī)學(xué)圖像分割、目標(biāo)檢測(cè)識(shí)別、目標(biāo)分類、圖像復(fù)原和超分辨率重建

李祎：女，1996年生，碩士生，研究方向?yàn)獒t(yī)學(xué)圖像分割

周治國：男，1977年生，副教授，研究方向?yàn)橹悄芨兄c導(dǎo)航

張?jiān)溃耗校?996年生，碩士生，研究方向?yàn)獒t(yī)學(xué)圖像處理、深度學(xué)習(xí)

夏卓巖：男，1997年生，碩士生，研究方向?yàn)閳D像分割、目標(biāo)檢測(cè)與分類、目標(biāo)識(shí)別

通訊作者:
時(shí)永剛　ygshi@bit.edu.cn

中圖分類號(hào): TN911.73; R735.34
計(jì)量
- 文章訪問數(shù): 1152
- HTML全文瀏覽量: 677
- PDF下載量: 200
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2021-09-01
- 修回日期: 2021-12-21
- 錄用日期: 2021-12-21
- 網(wǎng)絡(luò)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: 結(jié)腸息肉的精確分割對(duì)結(jié)直腸癌的診斷和治療具有重要意義，目前的分割方法普遍存在有偽影、分割精度低等問題。該文提出一種基于階梯結(jié)構(gòu)的U-Net結(jié)腸息肉分割算法(SU-Net)，使用U-Net的U型結(jié)構(gòu)，利用Kronecker乘積來擴(kuò)展標(biāo)準(zhǔn)空洞卷積核，構(gòu)成Kronecker空洞卷積下采樣有效擴(kuò)大感受野，彌補(bǔ)傳統(tǒng)空洞卷積容易丟失的細(xì)節(jié)特征；應(yīng)用具有階梯結(jié)構(gòu)的融合模塊，遵循擴(kuò)展和堆疊原則形成階梯狀的分層結(jié)構(gòu)，有效捕獲上下文信息并從多個(gè)尺度聚合特征；在解碼器引入卷積重構(gòu)上采樣模塊生成密集的像素級(jí)預(yù)測(cè)圖，捕獲雙線性插值上采樣中缺少的精細(xì)信息。在Kvasir-SEG數(shù)據(jù)集和CVC-EndoSceneStill數(shù)據(jù)集上對(duì)模型進(jìn)行了測(cè)試，相似系數(shù)(Dice)指標(biāo)和交并比(IoU)指標(biāo)分別達(dá)到了87.51%, 88.75%和82.30%, 85.64%。實(shí)驗(yàn)結(jié)果表明，該文所提方法改善了因過度曝光、低對(duì)比度引起的分割精度低的問題，同時(shí)消除了邊界外部的圖像偽影和圖像內(nèi)部不連貫的現(xiàn)象，優(yōu)于其他息肉分割方法。
- 圖像分割 /
- 結(jié)腸息肉圖像 /
- 空洞卷積 /
- 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

HTML全文

圖 1 SU-Net整體框架

下載: 全尺寸圖片幻燈片

圖 2 不同類型卷積核和KACD模塊

下載: 全尺寸圖片幻燈片

圖 3 階梯結(jié)構(gòu)的融合模塊

下載: 全尺寸圖片幻燈片

圖 4 卷積重構(gòu)上采樣模塊

下載: 全尺寸圖片幻燈片

圖 5 SU-Net與其他分割模型在EndoSceneStill數(shù)據(jù)集上的分割結(jié)果

下載: 全尺寸圖片幻燈片

圖 6 SU-Net與其他分割模型在Kvasir-SEG數(shù)據(jù)集上的分割結(jié)果

下載: 全尺寸圖片幻燈片

表 1 SU-Net消融實(shí)驗(yàn)列表

序號(hào)	實(shí)驗(yàn)描述
1	基線
2	僅將基線里的空洞卷積替換為Kronecker空洞卷積
3	將實(shí)驗(yàn)2中下采樣替換為Kronecker空洞卷積下采樣
4	在實(shí)驗(yàn)3中的編碼器解碼器之間加入階梯結(jié)構(gòu)的融合模塊
5	SU-Net

下載: 導(dǎo)出CSV

表 2 在EndoSceneStill數(shù)據(jù)集上各實(shí)驗(yàn)的量化結(jié)果

評(píng)估標(biāo)準(zhǔn)	消融實(shí)驗(yàn)編號(hào)
評(píng)估標(biāo)準(zhǔn)	1	2	3	4	5
召回率	0.7819	0.8195	0.8028	0.8027	0.8237
特異性	0.9931	0.9908	0.9946	0.9947	0.9929
精確率	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

下載: 導(dǎo)出CSV

表 3 在Kvasir-SEG數(shù)據(jù)集上各實(shí)驗(yàn)的量化結(jié)果

評(píng)估標(biāo)準(zhǔn)	消融實(shí)驗(yàn)編號(hào)
評(píng)估標(biāo)準(zhǔn)	1	2	3	4	5
召回率	0.8664	0.8631	0.8636	0.8750	0.8752
特異性	0.9840	0.9854	0.9844	0.9858	0.9866
精確率	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

下載: 導(dǎo)出CSV

表 4 不同模型在EndoSceneStill數(shù)據(jù)集中的量化評(píng)估結(jié)果

模型	召回率	特異性	精確率	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

下載: 導(dǎo)出CSV

表 5 不同模型在Kvasir-SEG數(shù)據(jù)集中的量化評(píng)估結(jié)果

模型	召回率	特異性	精確率	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

下載: 導(dǎo)出CSV

參考文獻(xiàn)(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.