Cell Image Segmentation Method Based on Residual Block and Attention Mechanism

Wenxiu Zhang; Zhencai Zhu; Yonghe Zhang; Xinyu Wang; Guopeng Ding

doi:10.3788/AOS202040.1710001

[1] Harder N, Bodnar M, Eils R et al. 3D segmentation and quantification of mouse embryonic stem cells in fluorescence microscopy images. [C]∥2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, March 30-April 2, 2011, Chicago, IL, USA. New York: IEEE, 216-219(2011).

[2] Faustino G M, Gattass M, Rehen S. Automatic embryonic stem cell detection and counting method in fluorescence microscopy images. [C]∥2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, June 28-July 1, 2009, Boston, MA, USA. New York: IEEE, 799-802(2012).

[3] Yin Z Z, Kanade T, Chen M. Understanding the phase contrast optics to restore artifact-free microscopy images for segmentation[J]. Medical Image Analysis, 16, 1047-1062(2012).

[4] Yin Z Z, Li K, Kanade T et al. Understanding the optics to aid microscopy image segmentation[M]. ∥Jiang T Z, Navab N, Pluim J P W, et al. Medical image computing and computer-assisted intervention-MICCAI 2010. Heidelberg: Springer, 209-217(2010).

[5] Yin Z Z, Su H, Ker E et al. Cell-sensitive microscopy imaging for cell image segmentation[M]. ∥Golland P, Hata N, Barillot C, et al. Medical image computing and computer-assisted intervention-MICCAI 2014. Cham: Springer, 8673, 41-48(2014).

[6] Wu C Y, Yi B S, Zhang Y G et al. Retinal vessel image segmentation based on improved convolutional neural network[J]. Acta Optica Sinica, 38, 1111004(2018).

[7] Zheng T Y, Tang C, Lei Z K. Multi-scale retinal vessel segmentation based on fully convolutional neural network[J]. Acta Optica Sinica, 39, 0211002(2019).

[8] Chu J H, Li X C, Zhang J Q et al. Fine-granted segmentation method for three-dimensional brain tumors using cascaded convolutional network[J]. Laser & Optoelectronics Progress, 56, 101001(2019).

[9] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 3431-3440(2017).

[10] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. ∥Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention - MICCAI 2015. Cham: Springer, 9351, 234-241(2015).

[11] Quan T M, Hildebrand D G C. -12-26)[2020-03-05]. https:∥arxiv., org/abs/1612, 05360(2016).

[12] Yi J R, Wu P X, Jiang M L et al. Attentive neural cell instance segmentation[J]. Medical Image Analysis, 55, 228-240(2019).

[13] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Cham: Springer, 9905, 21-37(2016).

[14] Alom M Z, Yakopcic C, Taha T M et al. Nuclei segmentation with recurrent residual convolutional neural networks based U-Net (R2U-Net). [C]∥NAECON 2018 - IEEE National Aerospace and Electronics Conference, July 23-26, 2018, Dayton, OH. New York: IEEE, 228-233(2018).

[15] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

[16] Chen L C, Yang Y, Wang J et al. Attention to scale: scale-aware semantic image segmentation. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 3640-3649(2016).

[17] Milletari F, Navab N, Ahmadi S A. V-Net: fully convolutional neural networks for volumetric medical image segmentation. [C]∥2016 Fourth International Conference on 3D Vision (3DV), October 25-28, 2016, Stanford, CA, USA. New York: IEEE, 770-778(2016).

[18] Kingma D P. -01-30)[ 2020-03-05]. https:∥arxiv., org/abs/1412, 6980(2017).

[19] Srivastava N, Hinton G, Krizhevsky A et al. Dropout: a simple way to prevent neural networks from overfitting[J]. The Journal of Machine Learning Research, 15, 1929-1958(2014).

[20] Garcia-Garcia A, Orts-Escolano S, Oprea S et al. A survey on deep learning techniques for image and video semantic segmentation[J]. Applied Soft Computing, 70, 41-65(2018).

[21] Levandowsky M, Winter D. Distance between sets[J]. Nature, 234, 34-35(1971).

[22] Dice L R. Measures of the amount of ecologic association between species[J]. Ecology, 26, 297-302(1945).

[23] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).