Abstract

Cell counting is a fundamental but crucial task for microscopic analysis. In this paper, we present a method that can count cells automatically and achieves good accuracy. The algorithm extends the U-net from the single-column to the multi-column to capture the features of cells with various sizes. The general convolutional layers in the U-net body are replaced by residual blocks to help the network converge better. Furthermore, a region-based loss function is designed to guide the model to slide into the proper local minima and avoid overfitting. Experimental results on three public datasets show that the proposed method can handle different kinds of images with promising accuracy. Compared with other state-of-the-art approaches, the proposed approach performs superiorly.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
  30. K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European conference on computer vision, (Springer, 2016), 630–645.
  31. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016), 770–778.
  32. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).
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  34. P. Kainz, M. Urschler, S. Schulter, P. Wohlhart, and V. Lepetit, “You should use regression to detect cells,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), 276–283.
  35. E. Lu, W. Xie, and A. Zisserman, “Class-agnostic counting,” in Asian conference on computer vision, (Springer, 2018), 669–684.

2020 (1)

2019 (2)

D. Kang, Z. Ma, and A. B. Chan, “Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks—Counting, Detection, and Tracking,” IEEE Transactions on Circuits and Systems for Video Technology 29(5), 1408–1422 (2019).
[Crossref]

R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Cell-Net: Embryonic Cell Counting and Centroid Localization via Residual Incremental Atrous Pyramid and Progressive Upsampling Convolution,” IEEE Access 7, 81945–81955 (2019).
[Crossref]

2018 (3)

W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Computer methods in biomechanics and biomedical engineering: Imaging & Visualization 6(3), 283–292 (2018).
[Crossref]

B. Yang, J. Cao, N. Wang, Y. Zhang, and L. Zou, “Counting challenging crowds robustly using a multi-column multi-task convolutional neural network,” Signal Processing: Image Communication 64, 118–129 (2018).
[Crossref]

X. Pan, D. Yang, L. Li, Z. Liu, H. Yang, Z. Cao, Y. He, Z. Ma, and Y. Chen, “Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks,” World Wide Web 21(6), 1721–1743 (2018).
[Crossref]

2016 (1)

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Detecting overlapping instances in microscopy images using extremal region trees,” Med. Image Anal. 27, 3–16 (2016).
[Crossref]

2010 (2)

C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE transactions on image processing 19(12), 3243–3254 (2010).
[Crossref]

C. Jung, C. Kim, S. W. Chae, and S. Oh, “Unsupervised segmentation of overlapped nuclei using Bayesian classification,” IEEE Trans. Biomed. Eng. 57(12), 2825–2832 (2010).
[Crossref]

1998 (1)

C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Transactions on image processing 7(3), 359–369 (1998).
[Crossref]

Akram, S. U.

S. U. Akram, J. Kannala, L. Eklund, and J. Heikkilä, “Cell segmentation proposal network for microscopy image analysis,” in Deep Learning and Data Labeling for Medical Applications (Springer, 2016), pp. 21–29.

Arteta, C.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Detecting overlapping instances in microscopy images using extremal region trees,” Med. Image Anal. 27, 3–16 (2016).
[Crossref]

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect partially overlapping instances,” in Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, 2013), 3230–3237.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Interactive object counting,” in European conference on computer vision, (Springer, 2014), 504–518.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect cells using non-overlapping extremal regions,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2012), 348–356.

Au, J.

R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Cell-Net: Embryonic Cell Counting and Centroid Localization via Residual Incremental Atrous Pyramid and Progressive Upsampling Convolution,” IEEE Access 7, 81945–81955 (2019).
[Crossref]

R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Blastomere cell counting and centroid localization in microscopic images of human embryo,” in 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP), (IEEE, 2018), 1–6.

Ba, J.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

Bengio, Y.

J. Paul Cohen, G. Boucher, C. A. Glastonbury, H. Z. Lo, and Y. Bengio, “Count-ception: Counting by fully convolutional redundant counting,” in Proceedings of the IEEE International Conference on Computer Vision Workshops, 2017), 18–26.

Bigras, G.

Y. Xue, N. Ray, J. Hugh, and G. Bigras, “Cell counting by regression using convolutional neural network,” in European Conference on Computer Vision, (Springer, 2016), 274–290.

Boucher, G.

J. Paul Cohen, G. Boucher, C. A. Glastonbury, H. Z. Lo, and Y. Bengio, “Count-ception: Counting by fully convolutional redundant counting,” in Proceedings of the IEEE International Conference on Computer Vision Workshops, 2017), 18–26.

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), 234–241.

Cao, J.

B. Yang, J. Cao, N. Wang, Y. Zhang, and L. Zou, “Counting challenging crowds robustly using a multi-column multi-task convolutional neural network,” Signal Processing: Image Communication 64, 118–129 (2018).
[Crossref]

Cao, Z.

X. Pan, D. Yang, L. Li, Z. Liu, H. Yang, Z. Cao, Y. He, Z. Ma, and Y. Chen, “Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks,” World Wide Web 21(6), 1721–1743 (2018).
[Crossref]

Chae, S. W.

C. Jung, C. Kim, S. W. Chae, and S. Oh, “Unsupervised segmentation of overlapped nuclei using Bayesian classification,” IEEE Trans. Biomed. Eng. 57(12), 2825–2832 (2010).
[Crossref]

Chan, A. B.

D. Kang, Z. Ma, and A. B. Chan, “Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks—Counting, Detection, and Tracking,” IEEE Transactions on Circuits and Systems for Video Technology 29(5), 1408–1422 (2019).
[Crossref]

Chan, T.

T. Chan and L. Vese, “An active contour model without edges,” in International Conference on Scale-Space Theories in Computer Vision, (Springer, 1999), 141–151.

Chaudry, Q.

S. Kothari, Q. Chaudry, and M. D. Wang, “Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques,” in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, (IEEE, 2009), 795–798.

Chen, S.

Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma, “Single-image crowd counting via multi-column convolutional neural network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 589–597.

Chen, Y.

X. Pan, D. Yang, L. Li, Z. Liu, H. Yang, Z. Cao, Y. He, Z. Ma, and Y. Chen, “Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks,” World Wide Web 21(6), 1721–1743 (2018).
[Crossref]

Eklund, L.

S. U. Akram, J. Kannala, L. Eklund, and J. Heikkilä, “Cell segmentation proposal network for microscopy image analysis,” in Deep Learning and Data Labeling for Medical Applications (Springer, 2016), pp. 21–29.

Fiaschi, L.

L. Fiaschi, U. Köthe, R. Nair, and F. A. Hamprecht, “Learning to count with regression forest and structured labels,” in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), (IEEE, 2012), 2685–2688.

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), 234–241.

Fox, M. D.

C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE transactions on image processing 19(12), 3243–3254 (2010).
[Crossref]

Gao, S.

Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma, “Single-image crowd counting via multi-column convolutional neural network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 589–597.

Genchev, G. Z.

Glastonbury, C. A.

J. Paul Cohen, G. Boucher, C. A. Glastonbury, H. Z. Lo, and Y. Bengio, “Count-ception: Counting by fully convolutional redundant counting,” in Proceedings of the IEEE International Conference on Computer Vision Workshops, 2017), 18–26.

Gui, C.

C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE transactions on image processing 19(12), 3243–3254 (2010).
[Crossref]

Hamprecht, F. A.

L. Fiaschi, U. Köthe, R. Nair, and F. A. Hamprecht, “Learning to count with regression forest and structured labels,” in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), (IEEE, 2012), 2685–2688.

M. von Borstel, M. Kandemir, P. Schmidt, M. K. Rao, K. Rajamani, and F. A. Hamprecht, “Gaussian process density counting from weak supervision,” in European Conference on Computer Vision, (Springer, 2016), 365–380.

Hao, A.

R. Zhu, D. Sui, H. Qin, and A. Hao, “An extended type cell detection and counting method based on FCN,” in 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), (IEEE, 2017), 51–56.

Havelock, J.

R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Cell-Net: Embryonic Cell Counting and Centroid Localization via Residual Incremental Atrous Pyramid and Progressive Upsampling Convolution,” IEEE Access 7, 81945–81955 (2019).
[Crossref]

R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Blastomere cell counting and centroid localization in microscopic images of human embryo,” in 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP), (IEEE, 2018), 1–6.

He, K.

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European conference on computer vision, (Springer, 2016), 630–645.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016), 770–778.

He, Y.

X. Pan, D. Yang, L. Li, Z. Liu, H. Yang, Z. Cao, Y. He, Z. Ma, and Y. Chen, “Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks,” World Wide Web 21(6), 1721–1743 (2018).
[Crossref]

Heikkilä, J.

S. U. Akram, J. Kannala, L. Eklund, and J. Heikkilä, “Cell segmentation proposal network for microscopy image analysis,” in Deep Learning and Data Labeling for Medical Applications (Springer, 2016), pp. 21–29.

Hugh, J.

Y. Xue, N. Ray, J. Hugh, and G. Bigras, “Cell counting by regression using convolutional neural network,” in European Conference on Computer Vision, (Springer, 2016), 274–290.

Jung, C.

C. Jung, C. Kim, S. W. Chae, and S. Oh, “Unsupervised segmentation of overlapped nuclei using Bayesian classification,” IEEE Trans. Biomed. Eng. 57(12), 2825–2832 (2010).
[Crossref]

Kainz, P.

P. Kainz, M. Urschler, S. Schulter, P. Wohlhart, and V. Lepetit, “You should use regression to detect cells,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), 276–283.

Kandemir, M.

M. von Borstel, M. Kandemir, P. Schmidt, M. K. Rao, K. Rajamani, and F. A. Hamprecht, “Gaussian process density counting from weak supervision,” in European Conference on Computer Vision, (Springer, 2016), 365–380.

Kang, D.

D. Kang, Z. Ma, and A. B. Chan, “Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks—Counting, Detection, and Tracking,” IEEE Transactions on Circuits and Systems for Video Technology 29(5), 1408–1422 (2019).
[Crossref]

Kannala, J.

S. U. Akram, J. Kannala, L. Eklund, and J. Heikkilä, “Cell segmentation proposal network for microscopy image analysis,” in Deep Learning and Data Labeling for Medical Applications (Springer, 2016), pp. 21–29.

Kim, C.

C. Jung, C. Kim, S. W. Chae, and S. Oh, “Unsupervised segmentation of overlapped nuclei using Bayesian classification,” IEEE Trans. Biomed. Eng. 57(12), 2825–2832 (2010).
[Crossref]

Kingma, D. P.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

Koltun, V.

F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” arXiv preprint arXiv:1511.07122 (2015).

Kong, Y.

Kothari, S.

S. Kothari, Q. Chaudry, and M. D. Wang, “Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques,” in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, (IEEE, 2009), 795–798.

Köthe, U.

L. Fiaschi, U. Köthe, R. Nair, and F. A. Hamprecht, “Learning to count with regression forest and structured labels,” in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), (IEEE, 2012), 2685–2688.

Lempitsky, V.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Detecting overlapping instances in microscopy images using extremal region trees,” Med. Image Anal. 27, 3–16 (2016).
[Crossref]

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect partially overlapping instances,” in Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, 2013), 3230–3237.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Interactive object counting,” in European conference on computer vision, (Springer, 2014), 504–518.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect cells using non-overlapping extremal regions,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2012), 348–356.

V. Lempitsky and A. Zisserman, “Learning to count objects in images,” in Neural Information Processing Systems 2010, 2010, 1324–1332.

Lepetit, V.

P. Kainz, M. Urschler, S. Schulter, P. Wohlhart, and V. Lepetit, “You should use regression to detect cells,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), 276–283.

Li, C.

C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE transactions on image processing 19(12), 3243–3254 (2010).
[Crossref]

Li, H.

Y. Kong, H. Li, Y. Ren, G. Z. Genchev, X. Wang, H. Zhao, Z. Xie, and H. Lu, “Automated yeast cells segmentation and counting using a parallel U-Net based two-stage framework,” OSA Continuum 3(4), 982–992 (2020).
[Crossref]

C. Zhang, H. Li, X. Wang, and X. Yang, “Cross-scene crowd counting via deep convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, 833–841.

Li, L.

X. Pan, D. Yang, L. Li, Z. Liu, H. Yang, Z. Cao, Y. He, Z. Ma, and Y. Chen, “Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks,” World Wide Web 21(6), 1721–1743 (2018).
[Crossref]

Liu, Z.

X. Pan, D. Yang, L. Li, Z. Liu, H. Yang, Z. Cao, Y. He, Z. Ma, and Y. Chen, “Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks,” World Wide Web 21(6), 1721–1743 (2018).
[Crossref]

Lo, H. Z.

J. Paul Cohen, G. Boucher, C. A. Glastonbury, H. Z. Lo, and Y. Bengio, “Count-ception: Counting by fully convolutional redundant counting,” in Proceedings of the IEEE International Conference on Computer Vision Workshops, 2017), 18–26.

Lu, E.

E. Lu, W. Xie, and A. Zisserman, “Class-agnostic counting,” in Asian conference on computer vision, (Springer, 2018), 669–684.

Lu, H.

Ma, Y.

Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma, “Single-image crowd counting via multi-column convolutional neural network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 589–597.

Ma, Z.

D. Kang, Z. Ma, and A. B. Chan, “Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks—Counting, Detection, and Tracking,” IEEE Transactions on Circuits and Systems for Video Technology 29(5), 1408–1422 (2019).
[Crossref]

X. Pan, D. Yang, L. Li, Z. Liu, H. Yang, Z. Cao, Y. He, Z. Ma, and Y. Chen, “Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks,” World Wide Web 21(6), 1721–1743 (2018).
[Crossref]

Nair, R.

L. Fiaschi, U. Köthe, R. Nair, and F. A. Hamprecht, “Learning to count with regression forest and structured labels,” in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), (IEEE, 2012), 2685–2688.

Noble, J. A.

W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Computer methods in biomechanics and biomedical engineering: Imaging & Visualization 6(3), 283–292 (2018).
[Crossref]

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Detecting overlapping instances in microscopy images using extremal region trees,” Med. Image Anal. 27, 3–16 (2016).
[Crossref]

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect partially overlapping instances,” in Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, 2013), 3230–3237.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Interactive object counting,” in European conference on computer vision, (Springer, 2014), 504–518.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect cells using non-overlapping extremal regions,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2012), 348–356.

Oh, S.

C. Jung, C. Kim, S. W. Chae, and S. Oh, “Unsupervised segmentation of overlapped nuclei using Bayesian classification,” IEEE Trans. Biomed. Eng. 57(12), 2825–2832 (2010).
[Crossref]

Pan, X.

X. Pan, D. Yang, L. Li, Z. Liu, H. Yang, Z. Cao, Y. He, Z. Ma, and Y. Chen, “Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks,” World Wide Web 21(6), 1721–1743 (2018).
[Crossref]

Paul Cohen, J.

J. Paul Cohen, G. Boucher, C. A. Glastonbury, H. Z. Lo, and Y. Bengio, “Count-ception: Counting by fully convolutional redundant counting,” in Proceedings of the IEEE International Conference on Computer Vision Workshops, 2017), 18–26.

Pham, T. D.

C. Zhang, C. Sun, R. Su, and T. D. Pham, “Segmentation of clustered nuclei based on curvature weighting,” in Proceedings of the 27th Conference on Image and Vision Computing New Zealand, 2012, 49–54.

Prince, J. L.

C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Transactions on image processing 7(3), 359–369 (1998).
[Crossref]

Qin, H.

R. Zhu, D. Sui, H. Qin, and A. Hao, “An extended type cell detection and counting method based on FCN,” in 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), (IEEE, 2017), 51–56.

Rad, R. M.

R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Cell-Net: Embryonic Cell Counting and Centroid Localization via Residual Incremental Atrous Pyramid and Progressive Upsampling Convolution,” IEEE Access 7, 81945–81955 (2019).
[Crossref]

R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Blastomere cell counting and centroid localization in microscopic images of human embryo,” in 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP), (IEEE, 2018), 1–6.

Rajamani, K.

M. von Borstel, M. Kandemir, P. Schmidt, M. K. Rao, K. Rajamani, and F. A. Hamprecht, “Gaussian process density counting from weak supervision,” in European Conference on Computer Vision, (Springer, 2016), 365–380.

Rao, M. K.

M. von Borstel, M. Kandemir, P. Schmidt, M. K. Rao, K. Rajamani, and F. A. Hamprecht, “Gaussian process density counting from weak supervision,” in European Conference on Computer Vision, (Springer, 2016), 365–380.

Ray, N.

Y. Xue, N. Ray, J. Hugh, and G. Bigras, “Cell counting by regression using convolutional neural network,” in European Conference on Computer Vision, (Springer, 2016), 274–290.

Ren, S.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016), 770–778.

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European conference on computer vision, (Springer, 2016), 630–645.

Ren, Y.

Ronneberger, O.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), 234–241.

Saeedi, P.

R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Cell-Net: Embryonic Cell Counting and Centroid Localization via Residual Incremental Atrous Pyramid and Progressive Upsampling Convolution,” IEEE Access 7, 81945–81955 (2019).
[Crossref]

R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Blastomere cell counting and centroid localization in microscopic images of human embryo,” in 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP), (IEEE, 2018), 1–6.

Schmidt, P.

M. von Borstel, M. Kandemir, P. Schmidt, M. K. Rao, K. Rajamani, and F. A. Hamprecht, “Gaussian process density counting from weak supervision,” in European Conference on Computer Vision, (Springer, 2016), 365–380.

Schulter, S.

P. Kainz, M. Urschler, S. Schulter, P. Wohlhart, and V. Lepetit, “You should use regression to detect cells,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), 276–283.

Smith, L. N.

L. N. Smith, “Cyclical learning rates for training neural networks,” in 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), (IEEE, 2017), 464–472.

Su, R.

C. Zhang, C. Sun, R. Su, and T. D. Pham, “Segmentation of clustered nuclei based on curvature weighting,” in Proceedings of the 27th Conference on Image and Vision Computing New Zealand, 2012, 49–54.

Sui, D.

R. Zhu, D. Sui, H. Qin, and A. Hao, “An extended type cell detection and counting method based on FCN,” in 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), (IEEE, 2017), 51–56.

Sun, C.

C. Zhang, C. Sun, R. Su, and T. D. Pham, “Segmentation of clustered nuclei based on curvature weighting,” in Proceedings of the 27th Conference on Image and Vision Computing New Zealand, 2012, 49–54.

Sun, J.

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European conference on computer vision, (Springer, 2016), 630–645.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016), 770–778.

Urschler, M.

P. Kainz, M. Urschler, S. Schulter, P. Wohlhart, and V. Lepetit, “You should use regression to detect cells,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), 276–283.

Vese, L.

T. Chan and L. Vese, “An active contour model without edges,” in International Conference on Scale-Space Theories in Computer Vision, (Springer, 1999), 141–151.

von Borstel, M.

M. von Borstel, M. Kandemir, P. Schmidt, M. K. Rao, K. Rajamani, and F. A. Hamprecht, “Gaussian process density counting from weak supervision,” in European Conference on Computer Vision, (Springer, 2016), 365–380.

Walach, E.

E. Walach and L. Wolf, “Learning to count with cnn boosting,” in European conference on computer vision, (Springer, 2016), 660–676.

Wang, M. D.

S. Kothari, Q. Chaudry, and M. D. Wang, “Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques,” in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, (IEEE, 2009), 795–798.

Wang, N.

B. Yang, J. Cao, N. Wang, Y. Zhang, and L. Zou, “Counting challenging crowds robustly using a multi-column multi-task convolutional neural network,” Signal Processing: Image Communication 64, 118–129 (2018).
[Crossref]

Wang, X.

Y. Kong, H. Li, Y. Ren, G. Z. Genchev, X. Wang, H. Zhao, Z. Xie, and H. Lu, “Automated yeast cells segmentation and counting using a parallel U-Net based two-stage framework,” OSA Continuum 3(4), 982–992 (2020).
[Crossref]

C. Zhang, H. Li, X. Wang, and X. Yang, “Cross-scene crowd counting via deep convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, 833–841.

Wohlhart, P.

P. Kainz, M. Urschler, S. Schulter, P. Wohlhart, and V. Lepetit, “You should use regression to detect cells,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), 276–283.

Wolf, L.

E. Walach and L. Wolf, “Learning to count with cnn boosting,” in European conference on computer vision, (Springer, 2016), 660–676.

Xie, W.

W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Computer methods in biomechanics and biomedical engineering: Imaging & Visualization 6(3), 283–292 (2018).
[Crossref]

E. Lu, W. Xie, and A. Zisserman, “Class-agnostic counting,” in Asian conference on computer vision, (Springer, 2018), 669–684.

Xie, Z.

Xu, C.

C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE transactions on image processing 19(12), 3243–3254 (2010).
[Crossref]

C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Transactions on image processing 7(3), 359–369 (1998).
[Crossref]

Xue, Y.

Y. Xue, N. Ray, J. Hugh, and G. Bigras, “Cell counting by regression using convolutional neural network,” in European Conference on Computer Vision, (Springer, 2016), 274–290.

Yang, B.

B. Yang, J. Cao, N. Wang, Y. Zhang, and L. Zou, “Counting challenging crowds robustly using a multi-column multi-task convolutional neural network,” Signal Processing: Image Communication 64, 118–129 (2018).
[Crossref]

Yang, D.

X. Pan, D. Yang, L. Li, Z. Liu, H. Yang, Z. Cao, Y. He, Z. Ma, and Y. Chen, “Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks,” World Wide Web 21(6), 1721–1743 (2018).
[Crossref]

Yang, H.

X. Pan, D. Yang, L. Li, Z. Liu, H. Yang, Z. Cao, Y. He, Z. Ma, and Y. Chen, “Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks,” World Wide Web 21(6), 1721–1743 (2018).
[Crossref]

Yang, X.

C. Zhang, H. Li, X. Wang, and X. Yang, “Cross-scene crowd counting via deep convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, 833–841.

Yu, F.

F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” arXiv preprint arXiv:1511.07122 (2015).

Zhang, C.

C. Zhang, H. Li, X. Wang, and X. Yang, “Cross-scene crowd counting via deep convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, 833–841.

C. Zhang, C. Sun, R. Su, and T. D. Pham, “Segmentation of clustered nuclei based on curvature weighting,” in Proceedings of the 27th Conference on Image and Vision Computing New Zealand, 2012, 49–54.

Zhang, X.

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European conference on computer vision, (Springer, 2016), 630–645.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016), 770–778.

Zhang, Y.

B. Yang, J. Cao, N. Wang, Y. Zhang, and L. Zou, “Counting challenging crowds robustly using a multi-column multi-task convolutional neural network,” Signal Processing: Image Communication 64, 118–129 (2018).
[Crossref]

Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma, “Single-image crowd counting via multi-column convolutional neural network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 589–597.

Zhao, H.

Zhou, D.

Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma, “Single-image crowd counting via multi-column convolutional neural network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 589–597.

Zhu, R.

R. Zhu, D. Sui, H. Qin, and A. Hao, “An extended type cell detection and counting method based on FCN,” in 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), (IEEE, 2017), 51–56.

Zisserman, A.

W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Computer methods in biomechanics and biomedical engineering: Imaging & Visualization 6(3), 283–292 (2018).
[Crossref]

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Detecting overlapping instances in microscopy images using extremal region trees,” Med. Image Anal. 27, 3–16 (2016).
[Crossref]

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect partially overlapping instances,” in Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, 2013), 3230–3237.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Interactive object counting,” in European conference on computer vision, (Springer, 2014), 504–518.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect cells using non-overlapping extremal regions,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2012), 348–356.

V. Lempitsky and A. Zisserman, “Learning to count objects in images,” in Neural Information Processing Systems 2010, 2010, 1324–1332.

E. Lu, W. Xie, and A. Zisserman, “Class-agnostic counting,” in Asian conference on computer vision, (Springer, 2018), 669–684.

Zou, L.

B. Yang, J. Cao, N. Wang, Y. Zhang, and L. Zou, “Counting challenging crowds robustly using a multi-column multi-task convolutional neural network,” Signal Processing: Image Communication 64, 118–129 (2018).
[Crossref]

Computer methods in biomechanics and biomedical engineering: Imaging & Visualization (1)

W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Computer methods in biomechanics and biomedical engineering: Imaging & Visualization 6(3), 283–292 (2018).
[Crossref]

IEEE Access (1)

R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Cell-Net: Embryonic Cell Counting and Centroid Localization via Residual Incremental Atrous Pyramid and Progressive Upsampling Convolution,” IEEE Access 7, 81945–81955 (2019).
[Crossref]

IEEE Trans. Biomed. Eng. (1)

C. Jung, C. Kim, S. W. Chae, and S. Oh, “Unsupervised segmentation of overlapped nuclei using Bayesian classification,” IEEE Trans. Biomed. Eng. 57(12), 2825–2832 (2010).
[Crossref]

IEEE Transactions on Circuits and Systems for Video Technology (1)

D. Kang, Z. Ma, and A. B. Chan, “Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks—Counting, Detection, and Tracking,” IEEE Transactions on Circuits and Systems for Video Technology 29(5), 1408–1422 (2019).
[Crossref]

IEEE Transactions on image processing (1)

C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Transactions on image processing 7(3), 359–369 (1998).
[Crossref]

C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE transactions on image processing 19(12), 3243–3254 (2010).
[Crossref]

Med. Image Anal. (1)

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Detecting overlapping instances in microscopy images using extremal region trees,” Med. Image Anal. 27, 3–16 (2016).
[Crossref]

OSA Continuum (1)

Signal Processing: Image Communication (1)

B. Yang, J. Cao, N. Wang, Y. Zhang, and L. Zou, “Counting challenging crowds robustly using a multi-column multi-task convolutional neural network,” Signal Processing: Image Communication 64, 118–129 (2018).
[Crossref]

World Wide Web (1)

X. Pan, D. Yang, L. Li, Z. Liu, H. Yang, Z. Cao, Y. He, Z. Ma, and Y. Chen, “Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks,” World Wide Web 21(6), 1721–1743 (2018).
[Crossref]

Other (25)

F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” arXiv preprint arXiv:1511.07122 (2015).

R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Blastomere cell counting and centroid localization in microscopic images of human embryo,” in 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP), (IEEE, 2018), 1–6.

E. Walach and L. Wolf, “Learning to count with cnn boosting,” in European conference on computer vision, (Springer, 2016), 660–676.

S. U. Akram, J. Kannala, L. Eklund, and J. Heikkilä, “Cell segmentation proposal network for microscopy image analysis,” in Deep Learning and Data Labeling for Medical Applications (Springer, 2016), pp. 21–29.

C. Zhang, H. Li, X. Wang, and X. Yang, “Cross-scene crowd counting via deep convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, 833–841.

Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma, “Single-image crowd counting via multi-column convolutional neural network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 589–597.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect cells using non-overlapping extremal regions,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2012), 348–356.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect partially overlapping instances,” in Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, 2013), 3230–3237.

M. von Borstel, M. Kandemir, P. Schmidt, M. K. Rao, K. Rajamani, and F. A. Hamprecht, “Gaussian process density counting from weak supervision,” in European Conference on Computer Vision, (Springer, 2016), 365–380.

Y. Xue, N. Ray, J. Hugh, and G. Bigras, “Cell counting by regression using convolutional neural network,” in European Conference on Computer Vision, (Springer, 2016), 274–290.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), 234–241.

T. Chan and L. Vese, “An active contour model without edges,” in International Conference on Scale-Space Theories in Computer Vision, (Springer, 1999), 141–151.

S. Kothari, Q. Chaudry, and M. D. Wang, “Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques,” in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, (IEEE, 2009), 795–798.

C. Zhang, C. Sun, R. Su, and T. D. Pham, “Segmentation of clustered nuclei based on curvature weighting,” in Proceedings of the 27th Conference on Image and Vision Computing New Zealand, 2012, 49–54.

V. Lempitsky and A. Zisserman, “Learning to count objects in images,” in Neural Information Processing Systems 2010, 2010, 1324–1332.

J. Paul Cohen, G. Boucher, C. A. Glastonbury, H. Z. Lo, and Y. Bengio, “Count-ception: Counting by fully convolutional redundant counting,” in Proceedings of the IEEE International Conference on Computer Vision Workshops, 2017), 18–26.

L. Fiaschi, U. Köthe, R. Nair, and F. A. Hamprecht, “Learning to count with regression forest and structured labels,” in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), (IEEE, 2012), 2685–2688.

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European conference on computer vision, (Springer, 2016), 630–645.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016), 770–778.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

L. N. Smith, “Cyclical learning rates for training neural networks,” in 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), (IEEE, 2017), 464–472.

P. Kainz, M. Urschler, S. Schulter, P. Wohlhart, and V. Lepetit, “You should use regression to detect cells,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), 276–283.

E. Lu, W. Xie, and A. Zisserman, “Class-agnostic counting,” in Asian conference on computer vision, (Springer, 2018), 669–684.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Interactive object counting,” in European conference on computer vision, (Springer, 2014), 504–518.

R. Zhu, D. Sui, H. Qin, and A. Hao, “An extended type cell detection and counting method based on FCN,” in 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), (IEEE, 2017), 51–56.

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Figures (9)

Fig. 1.
Fig. 1. (a) The cell image. (b) The cell labels. (c) The density map.
Fig. 2.
Fig. 2. The overview of the MM-Net
Fig. 3.
Fig. 3. The residual block
Fig. 4.
Fig. 4. The multi-column and multi-resolution architecture
Fig. 5.
Fig. 5. (a) The cell image. (b) The density of ground truth (c) The mask based on (b)
Fig. 6.
Fig. 6. The proposed model’s performance on VGG Cells dataset. (a)The input image. (b) The predicted density map. (c) The annotation. (d)The detection.
Fig. 7.
Fig. 7. The proposed model’s performance on MBM dataset. (a)The input image. (b)The predicted density map. (c) The annotation. (d)The detection.
Fig. 8.
Fig. 8. The proposed model’s performance on PhC-HeLa dataset. (a)The input image. (b)The predicted density map. (c) The annotation. (d)The detection.
Fig. 9.
Fig. 9. The predictions of U-Net + RB and the MM-Net on some test images

Tables (5)

Tables Icon

Table 1. The sizes of receptive fields on the three branches

Tables Icon

Table 2. The comparison results on VGG Cells dataset with state-of-the-art works.

Tables Icon

Table 3. The comparison results on MBM dataset with state-of-the-art works

Tables Icon

Table 4. The comparison results on PhC-Hela dataset

Tables Icon

Table 5. The results of ablation study on VGG Cells

Equations (9)

Equations on this page are rendered with MathJax. Learn more.

F ( x ) = i = 1 N G ( x i ; σ ) ,
x l + 1 = x l + F ( x l , W l ) ,
R F i = R F i 1 + ( K 1 ) × m = 1 i 1 S m ,
L = 1 N i = 1 N | | F ( X i ) F i | | 2 2 ,
L f g = j R f g ( y ^ j i y j i ) 2 N f g i ,
L b g = j R b g ( y ^ j i y j i ) 2 N b g i ,
L = 1 N i = 1 N λ ω i ( L f g + L b g ) ,
ω  =  N f g N c e l l ,
M A E = 1 N i = 1 N | P i T i | ,