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

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References 27

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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

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