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Multi-atlas segmentation of optic disc in retinal images via convolutional neural network

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Abstract

Multi-atlas segmentation is widely accepted as an essential image segmentation approach. Through leveraging on the information from the atlases instead of utilizing the model-based segmentation techniques, the multi-atlas segmentation could significantly enhance the accuracy of segmentation. However, label fusion, which plays an important role for multi-atlas segmentation still remains the primary challenge. Bearing this in mind, a deep learning-based approach is presented through integrating feature extraction and label fusion. The proposed deep learning architecture consists of two independent channels composing of continuous convolutional layers. To evaluate the performance our approach, we conducted comparison experiments between state-of-the-art techniques and the proposed approach on publicly available datasets. Experimental results demonstrate that the accuracy of the proposed approach outperforms state-of-the-art techniques both in efficiency and effectiveness.

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Abbreviations

CNN:

Convolutional Neural Network

ReLU:

Rectified Linear Unit

RA:

Region Agreement

RAD:

Relative Absolute Area

References

  1. Akhondi-Asl A, Hoyte L, Lockhart ME, Warfield SK (2014) A logarithmic opinion pool based staple algorithm for the fusion of segmentations with associated reliability weights. IEEE Trans Med Imaging 33(10):1997–2009

    Article  Google Scholar 

  2. Alom MZ, Hasan M, Yakopcic C, et al (2018) Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation[J]

  3. Artaechevarria X, Muñoz-Barrutia A, Ortiz-de Solorzano C (2008) Efficient classifier generation and weighted voting for atlas-based segmentation: two small steps faster and closer to the combination oracle. In: Medical imaging, international society for optics and photonics, pp 69141W–69141W

  4. Bengio Y, Lamblin P, Popovici D, Larochelle H, et al (2007) Greedy layer-wise training of deep networks. Adv Neural Inform Process Syst 19:153

    Google Scholar 

  5. Bing L, Yu X, Zhang P, et al. (2017) A semi-supervised convolutional neural network for hyperspectral image classification[J]. Remote Sens Lett 8(9):839–848

    Article  Google Scholar 

  6. Brebisson AD, Montana G (2015) Deep neural networks for anatomical brain segmentation. In: Computer vision and pattern recognition workshops, pp 20–28

  7. Centers for Disease Control, Prevention (2014) National diabetes statistics report: estimates of diabetes and its burden in the united states, Tech. rep., Department of Health and Human Services, Atlanta GA: U.S.

  8. Dahl G, Mohamed A-r, Hinton GE et al (2010) Phone recognition with the mean-covariance restricted Boltzmann machine. In: Advances in neural information processing systems, pp 469–477

  9. Dahl GE, Yu D, Deng L, Acero A (2012) Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans Audio, Speech, Lang Process 20(1):30–42

    Article  Google Scholar 

  10. Deng L, Seltzer ML, Yu D, Acero A, Mohamed A-R, Hinton GE (2010) Binary coding of speech spectrograms using a deep auto-encoder. In: Interspeech. Citeseer, pp 1692–1695

  11. Ding R, Pang C, Liu H (2018) Audio-visual keyword spotting based on multidimensional convolutional neural network[C]. In: 2018 25th IEEE international conference on image processing (ICIP)

  12. Dhungel N, Carneiro G, Bradley AP (2015) Deep learning and structured prediction for the segmentation of mass in mammograms. Springer International Publishing, Cham, pp 605–612

    Google Scholar 

  13. Du C, Gao S (2017) Image segmentation-based multi-focus image fusion through multi-scale convolutional neural network[J]. IEEE Access 5(99):15750–15761

    Article  Google Scholar 

  14. Gorthi S, Akhondi-Asl A, Thiran J-P, Warfield SK (2014) Optimal map parameters estimation in staple-learning from performance parameters versus image similarity information. In: Machine learning in medical imaging. Springer, pp 174–181

  15. Guo Y, Gao Y, Shen D (2017) Deformable mr prostate segmentation via deep feature learning and sparse patch matching. IEEE Trans Med Imaging 35(4):197–222

    Google Scholar 

  16. Heckemann RA, Hajnal JV, Aljabar P, Rueckert D, Hammers A (2006) Automatic anatomical brain mri segmentation combining label propagation and decision fusion. Neuroimage 33(1):115–126

    Article  Google Scholar 

  17. Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  MathSciNet  Google Scholar 

  18. Hou L, Samaras D, Kurc TM (2016) Patch-based convolutional neural network for whole slide tissue image classification[C]. Comput Vis Pattern Recogn

  19. Hou J-C, Wang S-S, Lai Y-H, et al (2018) Audio-visual speech enhancement based on multimodal deep convolutional neural network[J]

  20. Kauppi T, Kalesnykiene V, Kamarainen J-K, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, Kalviainen H, Pietilä J (2007) The DIARETDB1 diabetic retinopathy database and evaluation protocol. BMVC, 1–10

  21. Klein A, Mensh B, Ghosh S, Tourville J, Hirsch J (2005) Mindboggle: automated brain labeling with multiple atlases. BMC Med Imag 5(1):1

    Article  Google Scholar 

  22. Koch LM, Wright R, Vatansever D, Kyriakopoulou V, Malamateniou C, Patkee PA, Rutherford M, Hajnal JV, Aljabar P, Rueckert D (2014) Graph-based label propagation in fetal brain mr images. In: Machine learning in medical imaging. Springer, pp 9–16

  23. Langerak T, van der Heide UA, Kotte AN, Berendsen FF, Pluim JP (2011) Label fusion in multi-atlas based segmentation with user-defined local weights. In: 2011 IEEE international symposium on biomedical imaging: from nano to macro. IEEE, pp 1480–1483

  24. Langerak T, van der Heide UA, Kotte AN, Berendsen FF, Pluim JP (2011) Local atlas selection and performance estimation in multi-atlas based segmentation. In: 2011 IEEE international symposium on biomedical imaging: from nano to macro. IEEE, pp 669–672

  25. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  26. Li X, Mao Y, Liu Y et al (2017) Adaptive deep convolutional neural networks for scene-specific object detection[J]. IEEE Trans Circ Syst Video Technol PP(99):1–1

    Article  Google Scholar 

  27. Liu S, Deng W (2016) Very deep convolutional neural network based image classification using small training sample size[C]. Pattern Recognition

  28. Mei S, Jiang R, Ji J et al (2018) Invariant feature extraction for image classification via multi-channel convolutional neural network[C]. In: International symposium on intelligent signal processing & communication systems

  29. Quan TM, Hildebrand DGC, Jeong WK (2016) FusionNet: a deep fully residual convolutional neural network for image segmentation in connectomics[J]

  30. Rohlfing T, Brandt R, Menzel R, Maurer CR (2004) Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage 21(4):1428–1442

    Article  Google Scholar 

  31. Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM (2015) DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. Springer International Publishing, Cham, pp 556–564

    Google Scholar 

  32. Shi H, Ushio T, Endo M, et al (2017) A multichannel convolutional neural network for cross-language dialog state tracking[C]. In: Spoken language technology workshop

  33. Wachinger C, Reuter M, Klein T Deepnat: deep convolutional neural network for segmenting neuroanatomy. Neuroimage

  34. Wang H, Suh JW, Das SR, Pluta JB, Craige C, Yushkevich PA (2013) Multi-atlas segmentation with joint label fusion. IEEE Trans Pattern Anal Mach Intell 35(3):611–623

    Article  Google Scholar 

  35. Warfield SK, Zou KH, Wells WM (2004) Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23(7):903–921

    Article  Google Scholar 

  36. Weimer D, Scholz-Reiter B, Shpitalni M (2016) Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection[J]. CIRP Ann Manuf Technol 65(1):417–420

    Article  Google Scholar 

  37. Wu YC, Yin F, Liu CL (2016) Improving handwritten chinese text recognition using neural network language models and convolutional neural network shape models[J]. Pattern Recogn 65(C):251–264

    Google Scholar 

  38. Yan Z, Jagadeesh V, Decoste D, et al (2014) HD-CNN: hierarchical deep convolutional neural network for image classification[J]. Eprint Arxiv

  39. Yang H, Sun J, Li H et al (2018) Neural multi-atlas label fusion: application to cardiac MR images[J]. Med Image Anal, 60–75

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Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

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Authors and Affiliations

  1. Shandong TV University, Jinan, 250014, China

    Xinbo Yang

  2. College of Industry and Commerce, Shandong Management University, Jinan, 250357, China

    Yan Zhang

  3. Department of Electrical Engineering and Information Technology at Shandong University of Sci&Tech, Jinan, 250031, China

    Yan Zhang

Authors
  1. Xinbo Yang
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  2. Yan Zhang
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Corresponding author

Correspondence to Yan Zhang.

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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Cite this article

Yang, X., Zhang, Y. Multi-atlas segmentation of optic disc in retinal images via convolutional neural network. Multimed Tools Appl 80, 16537–16547 (2021). https://doi.org/10.1007/s11042-019-08606-w

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