基于改进U-Net网络的腺体细胞图像分割算法

doi:10.16180/j.cnki.issn1007-7820.2019.11.004

Abstract

Abstract:

This paper proposed a full convolutional neural network model with attention module to solve the problem that multi-scale targets and information loss affect the segmentation accuracy of gland images in the automatic segmentation process. This model followed the encoder-decoder structure. Firstly, the atrous spatial pyramid pooling was added to the encoder path, and the original residual convolution layer was replaced by the atrous residual convolution layer in the encoder path. Secondly, the attention module was added to the decoder path to make the model output high-resolution feature map and improve the segmentation accuracy of the multi-scale object. The experimental results showed that the proposed network model had fewer parameters, high segmentation precision and good robustness, besides, the average segmentation accuracy of gland images was as high as 89.7%.

Key words: full convolutional neural network, encoder-decoder structure, atrous spatial pyramid pooling, attention module, high-resolution feature map, high segmentation precision

CLC Number:

TP391

BEI Chenyuan,YU Haibin,PAN Mian,JIANG Jie,LÜ Bingyun. Gland Cell Image Segmentation Algorithm Based on Improved U-Net Network[J].Electronic Science and Technology, 2019, 32(11): 18-22.

Figures/Tables 7

Figure 1.

Figure 2.

Figure 3.

Table 1

Figure 4.

Table 2

Table 3

References 27

[1]	Winawer S J, Fletcher R H, Miller L , et al. Colorectal cancer screening: clinical guidelines and rationale[J]. Gastroenterology, 1997,113(4):594-642.
[2]	陈爱斌, 江霞 . 细胞分割算法研究方法综述[J].电子世界 2011(15):76-79.
	Chen Aibin, Jiang Xia . Review of research methods for cell segmentation algorithms[J].Electronic World, 2011(15):76-79.
[3]	Elston C W, Ellis I O . Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up[J]. Histopathology, 2010,19(5):403-410.
[4]	Gleason D F . Histologic grading of prostate cancer:A perspective[J]. Human Pathology, 1992,23(3):273-279.
[5]	Fleming M, Ravula S, Tatishchev S F , et al. Colorectal carcinoma: Pathologic aspects[J]. Journal of Gastrointestinal Oncology, 2012,3(3):153-153.
[6]	章毓晋 . 图像处理和分析基础[M]. 北京: 高等教育出版社, 2002.
	Zhang Yujin. The basis of image processing and analysis [M]. Beijing: Higher Education Press, 2002.
[7]	Gurcan M N, Boucheron L E, Can A , et al. Histopathological image analysis: a review[J].IEEE Reviews in Biomedical Engineering 2009(2):147-147.
[8]	Altunbay D, Cigir C, Sokmensuer C , et al. Color graphs for automated cancer diagnosis and grading[J]. IEEE Transactions on Biomedical Engineering, 2010,57(3):665-665.
[9]	Gunduz Demir C, Kandemir M, Tosun A B , et al. Automatic segmentation of colon glands using object-graphs[J]. Medical Image Analysis, 2010,14(1):1-12. doi: 10.1016/j.media.2009.09.001
[10]	Fu H, Qiu G, Shu J , et al. A novel polar space random field model for the detection of glandular structures[J]. IEEE Transactions on Medical Imaging, 2014,33(3):764-764. doi: 10.1109/TMI.2013.2296572
[11]	Khasawneh S, Al Wahadni A, Lloyd C H . A stochastic polygons model for glandular structures in colon histology images[J]. IEEE Transactions on Medical Imaging, 2015,34(11):2366-2378.
[12]	Ronneberger O, Fischer P, Brox T. U-Net:convolutional networks for biomedical image segmentation[M]. Berlin: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015,Springer International Publishing, 2015.
[13]	Chen H, Qi X, Yu L , et al. DCAN:Deep contour-aware networks for object instance segmentation from histology images[J]. Medical Image Analysis, 2017,36(9):135-146.
[14]	Xu Y, Li Y, Liu M, et al. Gland instance segmentation by deep multichannel side supervision [C].Athens: International Conference on Medical Image Computing and Computer-Assisted Intervention,Springer,Cham, 2016.
[15]	Xu Y, Li Y, Wang Y , et al. Gland instance segmentation using deep multichannel neural networks[J]. IEEE Transactions on Biomedical Engineering, 2017,64(12):2901-2912.
[16]	Raza S E A, Cheung L, Epstein D, et al. MIMONet:gland segmentation using multi-input-multi-output convolutional neural network [C].Edinburgh:Annual Conference on Medical Image Understanding and Analysis,Springer,Cham, 2017.
[17]	Chen L C, Papandreou G, Kokkinos I , et al. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018,40(4):834-848.
[18]	Drelie Gelasca E, Obara B, Fedorov D , et al. A biosegmentation benchmark for evaluation of bioimage analysis methods[J]. BMC Bioinformatics, 2009,10(1):368-368.
[19]	Sirinukunwattana K, Pluim J P, Chen H , et al. Gland segmentation in colon histology images: The glas challenge contest[J]. Medical Image Analysis, 2017,35(8):489-502.
[20]	Lecun Y, Bengio Y, Hinton G . Deep learning[J]. Nature, 2015,521(7553):436-436.
[21]	Yu F, Koltun V, Funkhouser T A. Dilated residual networks [C].Hawaii:Conference on Computer Vision and Pattern Recognition, 2017.
[22]	Wang P, Chen P, Yuan Y , et al. Understanding convolution for semantic segmentation[C].California:IEEE Winter Conference on Applications of Computer Vision ( WACV), 2018.
[23]	Hu J, Shen L, Sun G . Squeeze-and-excitation networks[J]. IEEE Transaction on Network, 2017,28(4):523-539.
[24]	Liu Z, Li X, Luo P, et al. Semantic image segmentation via deep parsing network [C].Santiago:Proceedings of the IEEE International Conference on Computer Vision, 2015.
[25]	Oktay O, Schlemper J, Folgoc L L , et al. Attention U-Net: learning where to look for the pancreas[J]. IEEE Transaction on Network, 2018,29(2):117-129.
[26]	Abadi , Martín , Barham P, et al.TensorFlow:A system for large-scale machine learning[J]. IEEE Transaction on Network, 2016,27(9):1024-1037.
[27]	何菁, 陈胜 . 一种全新的两步自动化医学图像分割方案[J]. 电子科技, 2016,29(7):85-87.
	He Jing, Chen Sheng . A new two-step automated medical image segmentation scheme[J]. Electronic Science and Technology, 2016,29(7):85-87.

方法	F₁score
U-Net	0.788
U-Net \dilated CNN	0.836
U-Net \attention	0.832
U-Net \attention \dilated CNN	0.871
U-Net \ASPP	0.847
U-Net \ASPP \dilated CNN	0.885
U-Net \ASPP \attention \dilated CNN	0.897

	F₁score				Object Dice				Object Hausdorff				Sum
	A	Rank	B	Rank	A	Rank	B	Rank	A	Rank	B	Rank	Sum
Proposed DeepLab-v3 FCN-8 SegNet U-Net Freidburg1 CUMedVision1 Freidburg2 CVML ExB3 ExB2 ExB1 LIB vision4GlaS	0.897 0.858 0.783 0.862 0.788 0.834 0.868 0.870 0.652 0.896 0.892 0.891 0.777 0.635	1 8 11 7 10 9 6 5 13 2 3 4 12 14	0.832 0.753 0.692 0.764 0.697 0.605 0.769 0.695 0.541 0.719 0.686 0.703 0.306 0.527	1 4 9 3 7 11 2 8 12 5 10 6 14 13	0.885 0.864 0.795 0.859 0.781 0.875 0.867 0.876 0.644 0.886 0.884 0.882 0.781 0.737	2 8 10 9 11 6 7 5 14 1 3 4 12 13	0.825 0.807 0.767 0.804 0.781 0.783 0.800 0.786 0.654 0.765 0.754 0.786 0.617 0.610	1 2 9 3 8 7 4 5 12 10 11 5 13 14	54.20 62.62 105.04 65.72 102.47 57.19 74.60 57.09 155.43 57.36 54.79 57.41 112.71 107.49	1 7 11 8 10 4 9 3 14 5 2 6 13 12	119.93 118.51 147.28 124.97 143.75 146.61 153.65 148.47 176.24 159.87 187.44 145.58 190.45 210.10	2 1 7 3 4 6 9 8 11 10 12 5 13 14	8 30 57 33 50 43 37 34 76 33 41 30 77 80

	F1score						Sum
	score	rank	score	rank	score	rank	Sum
Proposed	0.725	1	0.775	1	240.14	1	3
Deeplab-v3	0.648	2	0.745	2	281.45	3	7
SegNet	0.622	3	0.739	3	247.84	2	7
U-Net	0.600	4	0.654	4	354.09	4	12
FCN-8	0.558	5	0.640	5	436.43	5	15

Gland Cell Image Segmentation Algorithm Based on Improved U-Net Network

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 27

Related Articles 15

Metrics

Comments

Recommended 10

[1]	WU Weijia,YANG Jian,YUAN Tianchen,SHAO Zhihui. Research on Track Structure Damage Identification Based on Support Vector Machine [J]. Electronic Science and Technology, 2022, 35(2): 27-33.
[2]	LI Hui,WANG Yicheng. CNNCIFG-Attention Model for Text Sentiment Classifcation [J]. Electronic Science and Technology, 2022, 35(2): 46-51.
[3]	SHAO Zhihui,YANG Jian,YUAN Tianchen,WU Weijia. Sleeper Diseases Diagnosis Based on Permutation Entropy and Support Vector Machine [J]. Electronic Science and Technology, 2022, 35(2): 52-58.
[4]	ZONG Shengkang,CHENG Jianpeng,ZHANG Xiliang. Automatic Detection Method of Crane Track Altitude Difference Based on Spot Position [J]. Electronic Science and Technology, 2022, 35(1): 21-28.
[5]	FANG Xin,WU Yaohui,WU Haozhen. Calculation of Motor Temperature Field Based on Fluent [J]. Electronic Science and Technology, 2021, 34(12): 30-35.
[6]	PENG Rongjie,PENG Yaxiong,LU Anjiang. Face Recognition System Based on Improved PCA+SVM [J]. Electronic Science and Technology, 2021, 34(12): 56-61.
[7]	ZHANG Can,CHEN Wei,YIN Zhong. Semantic Segmentation of Cervical Cell Image Based on Weak Supervision [J]. Electronic Science and Technology, 2021, 34(12): 68-74.
[8]	ZHANG Ying,LIU Zilong,WAN Wei. UAV Vehicle Target Detection Based on Faster R-CNN [J]. Electronic Science and Technology, 2021, 34(11): 11-20.
[9]	XU Yangyang,SAN Hongjun,CHEN Jiupeng,XIE Feiya,WEI Shunxiang,WANG Wanglin,LIU Liang,CHEN Jia. Numerical Simulation Analysis of Temperature Field in LaserCladding of FL-DLight3-4000 Laser [J]. Electronic Science and Technology, 2021, 34(11): 1-10.
[10]	SUN Shuai,LIU Zilong,WAN Wei. Improvement of RLS Algorithm Based on Regularization Model [J]. Electronic Science and Technology, 2021, 34(11): 26-30.
[11]	WANG Xinzhang,GUO Qiang,XU Xiaozhuo. The Analysis of Field Changes for Induction Motor with Broken End Rings [J]. Electronic Science and Technology, 2021, 34(10): 18-26.
[12]	CHEN Shengli,WANG Xinzhang,XU Xiaozhuo. Electromagnetic Simulation Analysis of Permanent Magnet Synchronous Motor Demagnetization Fault [J]. Electronic Science and Technology, 2021, 34(10): 32-37.
[13]	ZHAO Chong,CHI Mengmeng,CHU Cong,ZHANG Peng. Research on Motion Simulation and Visual Recognition Algorithm of Guide Dog Walking Mechanism [J]. Electronic Science and Technology, 2021, 34(9): 66-72.
[14]	LIU Xuan,FU Dongxiang. Multi-Angle Virtual Glasses Trial Technology Based on Face Attitude Estimation [J]. Electronic Science and Technology, 2021, 34(9): 58-65.
[15]	HAN Shifan,FU Dongxiang. Research on Optical Lens Reconstruction Algorithm Based on NURBS [J]. Electronic Science and Technology, 2021, 34(9): 24-29.