Figure 1.
$ 3 \times 3 $ convolution kernels with different dilation factors 1, 2 and 3 respectively. Red dots indicate nonzero values
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Reproducible kernel Hilbert space based global and local image segmentation
February
2021, 15(1): 27-40.
doi: 10.3934/ipi.2020049
Automatic extraction of cell nuclei using dilated convolutional network
| 1. | Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA |
| 2. | Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas TX 75390, USA |
Pathological examination has been done manually by visual inspection of hematoxylin and eosin (H&E)-stained images. However, this process is labor intensive, prone to large variations, and lacking reproducibility in the diagnosis of a tumor. We aim to develop an automatic workflow to extract different cell nuclei found in cancerous tumors portrayed in digital renderings of the H&E-stained images. For a given image, we propose a semantic pixel-wise segmentation technique using dilated convolutions. The architecture of our dilated convolutional network (DCN) is based on SegNet, a deep convolutional encoder-decoder architecture. For the encoder, all the max pooling layers in the SegNet are removed and the convolutional layers are replaced by dilated convolution layers with increased dilation factors to preserve image resolution. For the decoder, all max unpooling layers are removed and the convolutional layers are replaced by dilated convolution layers with decreased dilation factors to remove gridding artifacts. We show that dilated convolutions are superior in extracting information from textured images. We test our DCN network on both synthetic data sets and a public available data set of H&E-stained images and achieve better results than the state of the art.
Keywords:
convolution,
dilated convolution,
semantic segmentation,
deep learning,
histopathology image.
Mathematics Subject Classification:
Primary: 62H30, 62H35; Secondary: 68T10.
Citation:
Rajendra K C Khatri, Brendan J Caseria, Yifei Lou, Guanghua Xiao, Yan Cao. Automatic extraction of cell nuclei using dilated convolutional network. Inverse Problems & Imaging,
2021, 15
(1)
: 27-40.
doi: 10.3934/ipi.2020049
![]()
References:
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R. Hamaguchi, A. Fujita, K. Nemoto, T. Imaizumi and S. Hikosaka, Effective use of dilated convolutions for segmenting small object instances in remote sensing imagery, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018, URL http://arxiv.org/abs/1709.00179.
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M. Jung and M. Kang,
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Y. Lecun, L. Bottou, Y. Bengio and P. Haffner,
Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86 (1998), 2278-2324.
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C. Liu, M. Ng and T. Zeng,
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show all references
References:
| [1] |
V. Badrinarayanan, A. Kendall and R. Cipolla, Segnet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 39 (2017), 2481–2495.
doi: 10.1109/TPAMI.2016.2644615. |
| [2] |
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A. L. Yuille, Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,, IEEE Trans. Pattern Anal. Mach. Intell., 40 (2018), 834–848.
doi: 10.1109/TPAMI.2017.2699184. |
| [3] |
R. Hamaguchi, A. Fujita, K. Nemoto, T. Imaizumi and S. Hikosaka, Effective use of dilated convolutions for segmenting small object instances in remote sensing imagery, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018, URL http://arxiv.org/abs/1709.00179.
doi: 10.1109/WACV.2018.00162. |
| [4] |
N. Japkowicz and S. Stephen,
The class imbalance problem: A systematic study, Intelligent Data Analysis, 6 (2002), 429-449.
doi: 10.3233/IDA-2002-6504. |
| [5] |
M. Jung and M. Kang,
Efficient nonsmooth nonconvex optimization for image restoration and segmentation, Journal of Scientific Computing, 62 (2015), 336-370.
doi: 10.1007/s10915-014-9860-y. |
| [6] |
A. Khan, N. Rajpoot, D. Treanor and D. Magee, A non-linear mapping approach to stain normalisation in digital histopathology images using image-specific colour deconvolution, IEEE Trans. Biomedical Engineering, 61 (2014), 1729-1738. Google Scholar |
| [7] |
D. P. Kingma and J. L. Ba, Adam: A method for stochastic optimization, in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015, URL http://arxiv.org/abs/1412.6980. Google Scholar |
| [8] |
A. Krizhevsky, I. Sutskever and G. E. Hinton, Imagenet classification with deep convolutional neural networks,, in Communications of the ACM, 2017, 1–9, URL http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf.
doi: 10.1145/3065386. |
| [9] |
N. Kumar, R. Verma, S. Sharma, S. Bhargava, A. Vahadane and A. Sethi,
A dataset and technique for generalized nuclear segmentation for computational pathology, IEEE Trans. Med. Imag., 36 (2017), 1550-1560.
doi: 10.1109/TMI.2017.2677499. |
| [10] |
Y. Lecun, L. Bottou, Y. Bengio and P. Haffner,
Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86 (1998), 2278-2324.
doi: 10.1109/5.726791. |
| [11] |
C. Liu, M. Ng and T. Zeng,
Weighted variational model for selective image segmentation with application to medical images, Pattern Recognition, 76 (2018), 367-379.
doi: 10.1016/j.patcog.2017.11.019. |
| [12] |
MATLAB, version 9.5 (R2018b), The MathWorks Inc., Natick, Massachusetts, 2018. Google Scholar |
| [13] |
H. Noh, S. Hong and B. Han, Learning deconvolution network for semantic segmentation,, in Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), ICCV '15, IEEE Computer Society, Washington, DC, USA, 2015, 1520–1528.
doi: 10.1109/ICCV.2015.178. |
| [14] |
N. Qian,
On the momentum term in gradient descent learning algorithms, Neural Networks: The Official Journal of the International Neural Network Society, 12 (1999), 145-151.
doi: 10.1016/S0893-6080(98)00116-6. |
| [15] |
E. Reinhard, M. Adhikhmin, B. Gooch and P. Shirley, Color transfer between images, IEEE Comput. Graph. Appl., 21 (2001), 34-41. Google Scholar |
| [16] |
O. Ronneberger, P. Fischer and T. Brox, U-net: Convolutional networks for biomedical image segmentation,, in Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol. 9351 of LNCS, Springer, 2015,234–241, URL http://lmb.informatik.uni-freiburg.de/Publications/2015/RFB15a, (available on arXiv: 1505.04597 [cs.CV]).
doi: 10.1007/978-3-319-24574-4_28. |
| [17] |
E. Shelhamer, J. Long and T. Darrell, Fully convolutional networks for semantic segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 39 (2017), 640–651.
doi: 10.1109/TPAMI.2016.2572683. |
| [18] |
K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015, URL http://arxiv.org/abs/1409.1556. Google Scholar |
| [19] |
F. Yu and V. Koltun, Multi-scale context aggregation by dilated convolutions, CoRR, abs/1511.07122. Google Scholar |
Figure 2.
A matrix $ A $ and its reordering $ B $ . It also shows a dilated convolution on $ A $ and its corresponding convolution on $ B $
Figure 3.
Source image (left), Target image (middle) and Normalized image by Reinhard method (Right)
Figure 4.
Sample training images in the triangle data set with a uniform foreground and a uniform background
Figure 5.
Sample training images in the triangle data set with a textured foreground and a textured background
Figure 6.
Test images (1st row) with corresponding segmentations using SegNet (2nd row), U-Net3 (3rd row), U-Net4 (4th row) and our dilated convolutional network (5th row)
Figure 7.
Test images (1st row) with corresponding segmentations using SegNet (2nd row), U-Net3 (3rd row), U-Net4 (4th row) and our dilated convolutional network (5th row)
Figure 8.
Sample normalized image patches and corresponding manual segmentations from the dataset
Figure 9.
Test images (1st column) with corresponding segmentations using SegNet (2nd column), U-Net3 (3rd column) and our dilated convolutional network (4th column). Ground truth contours are plotted in red
Table 1.
Comparison of the network architectures of the SegNet and our Dilated Convolutional network, where "Conv" means "Convolutions" and D is the dilation factor. Third column shows how to use matrix splitting and merging procedures to implement dilated convolutions through efficient conventional convolutions
| SegNet | Our DCN | Our DCN | |
| (for efficient training) | |||
| 128x128x3 Input | 128x128x3 Input | 128x128x3 Input | |
| (or 64x64 Input) | (or 64x64 Input) | (or 64x64) Input | |
| 64 3x3 Conv | 64 3x3 Conv, D=1 | 64 3x3 Conv | |
| Normalization & RELU | Normalization & RELU | Normalization & RELU | |
| ReLU | ReLU | ReLU | |
| 64 3x3 Conv | 64 3x3 Conv, D=1 | 64 3x3 Conv | |
| Normalization & RELU | Normalization & RELU | Normalization & RELU | |
| Max Pooling | Matrix Splitting | ||
| 64 3x3 Conv | 64 3x3 Conv, D=2 | 64 3x3 Conv | |
| Encoder | Normalization & RELU | Normalization & RELU | Normalization & RELU |
| 64 3x3 Conv | 64 3x3 Conv, D=2 | 64 3x3 Conv | |
| Normalization & RELU | Normalization & RELU | Normalization & RELU | |
| Max Pooling | Matrix Splitting | ||
| 64 3x3 Conv | 64 3x3 Conv, D=4 | 64 3x3 Conv | |
| Batch Normalization | Batch Normalization | Batch Normalization | |
| ReLU | ReLU | ReLU | |
| 64 3x3 Conv | 64 3x3 Conv, D=4 | 64 3x3 Conv | |
| Batch Normalization | Batch Normalization | Batch Normalization | |
| ReLU | ReLU | ReLU | |
| Max Pooling | |||
| Max Unpooling | |||
| 64 3x3 Conv | 64 3x3 Conv, D=4 | 64 3x3 Conv | |
| Normalization & RELU | Normalization & RELU | Normalization & RELU | |
| Matrix Merging | |||
| 64 3x3 Conv | 64 3x3 Conv, D=2 | 64 3x3 Conv | |
| Normalization & RELU | Normalization & RELU | Normalization & RELU | |
| Max Unpooling | Matrix Merging | ||
| 64 3x3 Conv | 64 3x3 Conv, D=1 | 64 3x3 Conv | |
| Decoder | Normalization & RELU | Normalization & RELU | Normalization & RELU |
| 64 3x3 Conv | 64 3x3 Conv, D=1 | 64 3x3 Conv | |
| Normalization & RELU | Normalization & RELU | Normalization & RELU | |
| Max Unpooling | |||
| 64 3x3 Conv | |||
| Normalization & RELU | |||
| 2 3x3 Conv | 2 1x1 Conv | 2 1x1 Conv | |
| Normalization & RELU | |||
| Softmax | Softmax | Softmax | |
| Pixel Classification | Pixel Classification | Pixel Classification |
| SegNet | Our DCN | Our DCN | |
| (for efficient training) | |||
| 128x128x3 Input | 128x128x3 Input | 128x128x3 Input | |
| (or 64x64 Input) | (or 64x64 Input) | (or 64x64) Input | |
| 64 3x3 Conv | 64 3x3 Conv, D=1 | 64 3x3 Conv | |
| Normalization & RELU | Normalization & RELU | Normalization & RELU | |
| ReLU | ReLU | ReLU | |
| 64 3x3 Conv | 64 3x3 Conv, D=1 | 64 3x3 Conv | |
| Normalization & RELU | Normalization & RELU | Normalization & RELU | |
| Max Pooling | Matrix Splitting | ||
| 64 3x3 Conv | 64 3x3 Conv, D=2 | 64 3x3 Conv | |
| Encoder | Normalization & RELU | Normalization & RELU | Normalization & RELU |
| 64 3x3 Conv | 64 3x3 Conv, D=2 | 64 3x3 Conv | |
| Normalization & RELU | Normalization & RELU | Normalization & RELU | |
| Max Pooling | Matrix Splitting | ||
| 64 3x3 Conv | 64 3x3 Conv, D=4 | 64 3x3 Conv | |
| Batch Normalization | Batch Normalization | Batch Normalization | |
| ReLU | ReLU | ReLU | |
| 64 3x3 Conv | 64 3x3 Conv, D=4 | 64 3x3 Conv | |
| Batch Normalization | Batch Normalization | Batch Normalization | |
| ReLU | ReLU | ReLU | |
| Max Pooling | |||
| Max Unpooling | |||
| 64 3x3 Conv | 64 3x3 Conv, D=4 | 64 3x3 Conv | |
| Normalization & RELU | Normalization & RELU | Normalization & RELU | |
| Matrix Merging | |||
| 64 3x3 Conv | 64 3x3 Conv, D=2 | 64 3x3 Conv | |
| Normalization & RELU | Normalization & RELU | Normalization & RELU | |
| Max Unpooling | Matrix Merging | ||
| 64 3x3 Conv | 64 3x3 Conv, D=1 | 64 3x3 Conv | |
| Decoder | Normalization & RELU | Normalization & RELU | Normalization & RELU |
| 64 3x3 Conv | 64 3x3 Conv, D=1 | 64 3x3 Conv | |
| Normalization & RELU | Normalization & RELU | Normalization & RELU | |
| Max Unpooling | |||
| 64 3x3 Conv | |||
| Normalization & RELU | |||
| 2 3x3 Conv | 2 1x1 Conv | 2 1x1 Conv | |
| Normalization & RELU | |||
| Softmax | Softmax | Softmax | |
| Pixel Classification | Pixel Classification | Pixel Classification |
Table 2.
Quantitative metrics of the segmentation results on triangle data sets. Best values are displayed in bold
| Triangle | Global | Mean | Mean | Weighted | Mean | |
| Dataset | Accuracy | Accuracy | IoU | IoU | BFScore | |
| SegNet | Uniform | 0.9325 | 0.9508 | 0.7829 | 0.8882 | 0.4172 |
| U-Net3 | Uniform | 0.9694 | 0.9531 | 0.8784 | 0.9438 | 0.6572 |
| U-Net4 | Uniform | 0.9974 | 0.9953 | 0.9884 | 0.9949 | 0.9488 |
| Our DCN | Uniform | 0.9952 | 0.9941 | 0.9786 | 0.9906 | 0.8946 |
| SegNet | Textured | 0.8764 | 0.9280 | 0.6818 | 0.8139 | 0.3605 |
| U-Net3 | Textured | 0.8119 | 0.8614 | 0.5855 | 0.7359 | 0.2157 |
| U-Net4 | Textured | 0.7250 | 0.8148 | 0.4945 | 0.6391 | 0.2000 |
| Our DCN | Textured | 0.9658 | 0.9741 | 0.8728 | 0.9386 | 0.4638 |
| Triangle | Global | Mean | Mean | Weighted | Mean | |
| Dataset | Accuracy | Accuracy | IoU | IoU | BFScore | |
| SegNet | Uniform | 0.9325 | 0.9508 | 0.7829 | 0.8882 | 0.4172 |
| U-Net3 | Uniform | 0.9694 | 0.9531 | 0.8784 | 0.9438 | 0.6572 |
| U-Net4 | Uniform | 0.9974 | 0.9953 | 0.9884 | 0.9949 | 0.9488 |
| Our DCN | Uniform | 0.9952 | 0.9941 | 0.9786 | 0.9906 | 0.8946 |
| SegNet | Textured | 0.8764 | 0.9280 | 0.6818 | 0.8139 | 0.3605 |
| U-Net3 | Textured | 0.8119 | 0.8614 | 0.5855 | 0.7359 | 0.2157 |
| U-Net4 | Textured | 0.7250 | 0.8148 | 0.4945 | 0.6391 | 0.2000 |
| Our DCN | Textured | 0.9658 | 0.9741 | 0.8728 | 0.9386 | 0.4638 |
Table 3.
Quantitative metrics of the segmentation results on the H&E-stained image data set. Best values are displayed in bold
| Image | Global | Mean | Mean | Weighted | Mean | |
| Set | Accuracy | Accuracy | IoU | IoU | BFScore | |
| SegNet | Lung | 0.8819 | 0.8975 | 0.7324 | 0.8074 | 0.9204 |
| U-Net3 | Lung | 0.8917 | 0.9013 | 0.7475 | 0.8190 | 0.9266 |
| U-Net4 | Lung | 0.8929 | 0.8966 | 0.7484 | 0.8193 | 0.9343 |
| Our DCN | Lung | 0.9045 | 0.9033 | 0.7690 | 0.8355 | 0.9448 |
| SegNet | Breast | 0.8691 | 0.8990 | 0.7002 | 0.7917 | 0.8900 |
| U-Net3 | Breast | 0.8829 | 0.9051 | 0.7183 | 0.8124 | 0.8743 |
| U-Net4 | Breast | 0.8775 | 0.8977 | 0.7086 | 0.8057 | 0.8700 |
| Our DCN | Breast | 0.9047 | 0.9123 | 0.7538 | 0.8415 | 0.9210 |
| SegNet | Kidney | 0.9122 | 0.9249 | 0.7290 | 0.8634 | 0.9425 |
| U-Net3 | Kidney | 0.9133 | 0.9281 | 0.7259 | 0.8639 | 0.9218 |
| U-Net4 | Kidney | 0.8993 | 0.9145 | 0.7013 | 0.8462 | 0.9306 |
| Our DCN | Kidney | 0.9329 | 0.9277 | 0.7725 | 0.8911 | 0.9634 |
| SegNet | Prostate | 0.8956 | 0.9142 | 0.7533 | 0.8271 | 0.9105 |
| U-Net3 | Prostate | 0.8949 | 0.9041 | 0.7496 | 0.8255 | 0.9047 |
| U-Net4 | Prostate | 0.8961 | 0.9032 | 0.7510 | 0.8271 | 0.9090 |
| Our DCN | Prostate | 0.9211 | 0.9163 | 0.7962 | 0.8632 | 0.9336 |
| SegNet | Overall | 0.8897 | 0.9000 | 0.7383 | 0.8184 | 0.9159 |
| U-Net3 | Overall | 0.8957 | 0.8976 | 0.7467 | 0.8264 | 0.9069 |
| U-Net4 | Overall | 0.8914 | 0.8905 | 0.7380 | 0.8201 | 0.9110 |
| Our DCN | Overall | 0.9158 | 0.9039 | 0.7815 | 0.8548 | 0.9407 |
| Image | Global | Mean | Mean | Weighted | Mean | |
| Set | Accuracy | Accuracy | IoU | IoU | BFScore | |
| SegNet | Lung | 0.8819 | 0.8975 | 0.7324 | 0.8074 | 0.9204 |
| U-Net3 | Lung | 0.8917 | 0.9013 | 0.7475 | 0.8190 | 0.9266 |
| U-Net4 | Lung | 0.8929 | 0.8966 | 0.7484 | 0.8193 | 0.9343 |
| Our DCN | Lung | 0.9045 | 0.9033 | 0.7690 | 0.8355 | 0.9448 |
| SegNet | Breast | 0.8691 | 0.8990 | 0.7002 | 0.7917 | 0.8900 |
| U-Net3 | Breast | 0.8829 | 0.9051 | 0.7183 | 0.8124 | 0.8743 |
| U-Net4 | Breast | 0.8775 | 0.8977 | 0.7086 | 0.8057 | 0.8700 |
| Our DCN | Breast | 0.9047 | 0.9123 | 0.7538 | 0.8415 | 0.9210 |
| SegNet | Kidney | 0.9122 | 0.9249 | 0.7290 | 0.8634 | 0.9425 |
| U-Net3 | Kidney | 0.9133 | 0.9281 | 0.7259 | 0.8639 | 0.9218 |
| U-Net4 | Kidney | 0.8993 | 0.9145 | 0.7013 | 0.8462 | 0.9306 |
| Our DCN | Kidney | 0.9329 | 0.9277 | 0.7725 | 0.8911 | 0.9634 |
| SegNet | Prostate | 0.8956 | 0.9142 | 0.7533 | 0.8271 | 0.9105 |
| U-Net3 | Prostate | 0.8949 | 0.9041 | 0.7496 | 0.8255 | 0.9047 |
| U-Net4 | Prostate | 0.8961 | 0.9032 | 0.7510 | 0.8271 | 0.9090 |
| Our DCN | Prostate | 0.9211 | 0.9163 | 0.7962 | 0.8632 | 0.9336 |
| SegNet | Overall | 0.8897 | 0.9000 | 0.7383 | 0.8184 | 0.9159 |
| U-Net3 | Overall | 0.8957 | 0.8976 | 0.7467 | 0.8264 | 0.9069 |
| U-Net4 | Overall | 0.8914 | 0.8905 | 0.7380 | 0.8201 | 0.9110 |
| Our DCN | Overall | 0.9158 | 0.9039 | 0.7815 | 0.8548 | 0.9407 |
Table 4.
Comparison of the training and testing time of SegNet, U-Net3 and our DCN
| SegNet | U-Net3 | Our DCN | Our DCN (efficient) | |
| Training Time | 15 min 14 sec | 18 min 36 sec | 26 min 47 sec | 19 min 45 sec |
| Testing Time | 7.6 sec | 37.7 sec | 10.1 sec | 19.9 sec |
| SegNet | U-Net3 | Our DCN | Our DCN (efficient) | |
| Training Time | 15 min 14 sec | 18 min 36 sec | 26 min 47 sec | 19 min 45 sec |
| Testing Time | 7.6 sec | 37.7 sec | 10.1 sec | 19.9 sec |
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