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Inception U-Net Architecture for Semantic Segmentation to Identify Nuclei in Microscopy Cell Images

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Published:17 February 2020Publication History
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Abstract

With the increasing applications of deep learning in biomedical image analysis, in this article we introduce an inception U-Net architecture for automating nuclei detection in microscopy cell images of varying size and modality to help unlock faster cures, inspired from Kaggle Data Science Bowl Challenge 2018 (KDSB18). This study follows from the fact that most of the analysis requires nuclei detection as the starting phase for getting an insight into the underlying biological process and further diagnosis. The proposed architecture consists of a switch normalization layer, convolution layers, and inception layers (concatenated 1x1, 3x3, and 5x5 convolution and the hybrid of a max and Hartley spectral pooling layer) connected in the U-Net fashion for generating the image masks. This article also illustrates the model perception of image masks using activation maximization and filter map visualization techniques. A novel objective function segmentation loss is proposed based on the binary cross entropy, dice coefficient, and intersection over union loss functions. The intersection over union score, loss value, and pixel accuracy metrics evaluate the model over the KDSB18 dataset. The proposed inception U-Net architecture exhibits quite significant results as compared to the original U-Net and recent U-Net++ architecture.

References

  1. Nadeem Akhtar and U. Ragavendran. 2019. Interpretation of intelligence in CNN-pooling processes: A methodological survey. Neural Computing and Applications 32, 3 (2019), 879--898.Google ScholarGoogle ScholarCross RefCross Ref
  2. Syed Muhammad Anwar, Muhammad Majid, Adnan Qayyum, Muhammad Awais, Majdi Alnowami, and Muhammad Khurram Khan. 2018. Medical image analysis using convolutional neural networks: A review. Journal of Medical Systems 42, 11 (2018), 226.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. Layer normalization. arXiv:1607.06450.Google ScholarGoogle Scholar
  4. Sean Bell, C. Lawrence Zitnick, Kavita Bala, and Ross Girshick. 2016. Inside-Outside Net: Detecting objects in context with skip pooling and recurrent neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2874--2883.Google ScholarGoogle ScholarCross RefCross Ref
  5. Rohan Bhardwaj, Ankita R. Nambiar, and Debojyoti Dutta. 2017. A study of machine learning in healthcare. In Proceedings of the 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC’17), Vol. 2. IEEE, Los Alamitos, CA, 236--241.Google ScholarGoogle ScholarCross RefCross Ref
  6. Jose Dolz, Karthik Gopinath, Jing Yuan, Herve Lombaert, Christian Desrosiers, and Ismail Ben Ayed. 2018. HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation. IEEE Transactions on Medical Imaging 38, 5 (2018), 1116--1126.Google ScholarGoogle ScholarCross RefCross Ref
  7. Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau, and Sebastian Thrun. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 7639 (2017), 115.Google ScholarGoogle Scholar
  8. GitHub. 2018. UNetPlusPlus. Retrieved September 4, 2019 from https://github.com/MrGiovanni/UNetPlusPlus.Google ScholarGoogle Scholar
  9. Simon Graham, Quoc Dang Vu, Shan E. Ahmed Raza, Jin Tae Kwak, and Nasir Rajpoot. 2018. XY network for nuclear segmentation in multi-tissue histology images. arXiv:1812.06499.Google ScholarGoogle Scholar
  10. Zaiwang Gu, Jun Cheng, Huazhu Fu, Kang Zhou, Huaying Hao, Yitian Zhao, Tianyang Zhang, Shenghua Gao, and Jiang Liu. 2019. CE-Net: Context encoder network for 2D medical image segmentation. IEEE Transactions on Medical Imaging 38, 10 (2019), 2281--2292.Google ScholarGoogle ScholarCross RefCross Ref
  11. Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, and Hugo Larochelle. 2017. Brain tumor segmentation with deep neural networks. Medical Image Analysis 35 (2017), 18--31.Google ScholarGoogle ScholarCross RefCross Ref
  12. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In Proceedings of the IEEE International Conference on Computer Vision. 1026--1034.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167.Google ScholarGoogle Scholar
  14. Hongda Jiang, Ruichen Rong, Junyan Wu, Xiaoxiao Li, Xu Dong, and Eric Z. Chen. 2018. Skin lesion segmentation with improved C-UNet networks. bioRxiv. Retrieved January 27, 2020 from https://www.biorxiv.org/content/10.1101/382549v1.Google ScholarGoogle Scholar
  15. Kaggle. 2018. Kaggle Data Science Bowl Challenge—KDSB. Retrieved May 5, 2019 from https://www.kaggle.com/c/data-science-bowl-2018.Google ScholarGoogle Scholar
  16. Kaggle. 2018. KDSB—Another IoU Metric. Retrieved May 5, 2019 from https://www.kaggle.com/aglotero/another-iou-metric.Google ScholarGoogle Scholar
  17. Kaggle. 2018. KDSB Challenge—Rank 1 Model. Retrieved September 6, 2019 from https://www.kaggle.com/c/data-science-bowl-2018/discussion/54741.Google ScholarGoogle Scholar
  18. Kaggle. 2018. KDSB ChallengeRank 4 Model. Retrieved September 6, 2019 from https://www.kaggle.com/c/data-science-bowl-2018/discussion/55118.Google ScholarGoogle Scholar
  19. Konstantinos Kamnitsas, Christian Ledig, Virginia F. J. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Daniel Rueckert, and Ben Glocker. 2017. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis 36 (2017), 61--78.Google ScholarGoogle ScholarCross RefCross Ref
  20. Justin Ker, Lipo Wang, Jai Rao, and Tchoyoson Lim. 2017. Deep learning applications in medical image analysis. IEEE Access 6 (2017), 9375--9389.Google ScholarGoogle ScholarCross RefCross Ref
  21. Tianwei Lin, Xu Zhao, and Zheng Shou. 2017. Temporal convolution based action proposal: Submission to ActivityNet 2017. arXiv:1707.06750.Google ScholarGoogle Scholar
  22. Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I. Sánchez. 2017. A survey on deep learning in medical image analysis. Medical Image Analysis 42 (2017), 60--88.Google ScholarGoogle ScholarCross RefCross Ref
  23. Ping Luo, Jiamin Ren, Zhanglin Peng, Ruimao Zhang, and Jingyu Li. 2018. Differentiable learning-to-normalize via switchable normalization. arXiv:1806.10779.Google ScholarGoogle Scholar
  24. Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. 2016. V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In Proceedings of the 2016 4th International Conference on 3D Vision (3DV’16). IEEE, Los Alamitos, CA, 565--571.Google ScholarGoogle ScholarCross RefCross Ref
  25. Jordi Pont-Tuset and Ferran Marques. 2015. Supervised evaluation of image segmentation and object proposal techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence 38, 7 (2015), 1465--1478.Google ScholarGoogle ScholarCross RefCross Ref
  26. Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 234--241.Google ScholarGoogle ScholarCross RefCross Ref
  27. Sebastian Ruder. 2016. An overview of gradient descent optimization algorithms. arXiv:1609.04747.Google ScholarGoogle Scholar
  28. Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision. 618--626.Google ScholarGoogle ScholarCross RefCross Ref
  29. Benjamin Shickel, Patrick James Tighe, Azra Bihorac, and Parisa Rashidi. 2017. Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics 22, 5 (2017), 1589--1604.Google ScholarGoogle ScholarCross RefCross Ref
  30. Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv:1312.6034.Google ScholarGoogle Scholar
  31. Irina Solovei, Audrey S. Wang, Katharina Thanisch, Christine S. Schmidt, Stefan Krebs, Monika Zwerger, Tatiana V. Cohen, et al. 2013. LBR and lamin A/C sequentially tether peripheral heterochromatin and inversely regulate differentiation. Cell 152, 3 (2013), 584--598.Google ScholarGoogle ScholarCross RefCross Ref
  32. Kenji Suzuki. 2017. Overview of deep learning in medical imaging. Radiological Physics and Technology 10, 3 (2017), 257--273.Google ScholarGoogle ScholarCross RefCross Ref
  33. Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1--9.Google ScholarGoogle ScholarCross RefCross Ref
  34. Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2016. Instance normalization: The missing ingredient for fast stylization. arXiv:1607.08022.Google ScholarGoogle Scholar
  35. Nicole Gray Weiskopf and Chunhua Weng. 2013. Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research. Journal of the American Medical Informatics Association 20, 1 (2013), 144--151.Google ScholarGoogle ScholarCross RefCross Ref
  36. Zitao Zeng, Weihao Xie, Yunzhe Zhang, and Yao Lu. 2019. RIC-Unet: An improved neural network based on Unet for nuclei segmentation in histology images. IEEE Access 7 (2019), 21420--21428.Google ScholarGoogle ScholarCross RefCross Ref
  37. Hao Zhang and Jianwei Ma. 2018. Hartley spectral pooling for deep learning. arXiv:1810.04028.Google ScholarGoogle Scholar
  38. Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. 2018. Unet++: A nested U-Net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer, 3--11.Google ScholarGoogle Scholar
  39. Monika Zwerger, Chin Yee Ho, and Jan Lammerding. 2011. Nuclear mechanics in disease. Annual Review of Biomedical Engineering 13 (2011), 397--428.Google ScholarGoogle ScholarCross RefCross Ref

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