Have a personal or library account? Click to login
Applying 3D U-Net Architecture to the Task of Multi-Organ Segmentation in Computed Tomography Cover

Applying 3D U-Net Architecture to the Task of Multi-Organ Segmentation in Computed Tomography

By: Pavlo Radiuk  
Open Access
|Jun 2020

References

  1. [1] D. Shen, G. Wu, and H.-I. Suk, “Deep learning in medical image analysis,” Annual Review of Biomedical Engineering, vol. 19, no. 1, pp. 221–248, Jun. 2017. https://doi.org/10.1146/annurev-bioeng-071516-04444210.1146/annurev-bioeng-071516-044442547972228301734
  2. [2] W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, and F. E. Alsaadi, “A survey of deep neural network architectures and their applications,” Neurocomputing, vol. 234, pp. 11–26, Apr. 2017. https://doi.org/10.1016/j.neucom.2016.12.03810.1016/j.neucom.2016.12.038
  3. [3] E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 4, pp. 640–651, Apr. 2017. https://doi.org/10.1109/TPAMI.2016.257268310.1109/TPAMI.2016.257268327244717
  4. [4] H. Suk, S. W. Lee, and D. Shen, “Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis,” NeuroImage, vol. 101, pp. 569–582, Nov. 2014. https://doi.org/10.1016/j.neuroimage.2014.06.07710.1016/j.neuroimage.2014.06.077416584225042445
  5. [5] A. Hamidinekoo, E. Denton, A. Rampun, K. Honnor, and R. Zwiggelaar, “Deep learning in mammography and breast histology, an overview and future trends,” Medical Image Analysis, vol. 47, pp. 45–67, Jul. 2018. https://doi.org/10.1016/j.media.2018.03.00610.1016/j.media.2018.03.00629679847
  6. [6] G. Litjens et al., “State-of-the-art deep learning in cardiovascular image analysis,” JACC Cardiovascular Imaging, vol. 12, no. 8 Part 1, pp. 1549–1565, Aug. 2019. https://doi.org/10.1016/j.jcmg.2019.06.00910.1016/j.jcmg.2019.06.00931395244
  7. [7] J.-Z. Cheng et al., “Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans,” Scientific Reports, vol. 6, no. 24454, Apr. 2016. https://doi.org/10.1038/srep2445410.1038/srep24454483219927079888
  8. [8] T. Hirasawa et al., “Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images,” Gastric Cancer, vol. 21, no. 4, pp. 653–660, Jan. 2018. https://doi.org/10.1007/s10120-018-0793-210.1007/s10120-018-0793-229335825
  9. [9] Y. Hu et al., “Weakly-supervised convolutional neural networks for multimodal image registration,” Medical Image Analysis, vol. 49, pp. 1–13, Oct. 2018. https://doi.org/10.1016/J.MEDIA.2018.07.00210.1016/j.media.2018.07.002674251030007253
  10. [10] H. Takiyama et al., “Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks,” Scientific Reports, vol. 8, no. 7497, pp. 1–8, May. 2018. https://doi.org/10.1038/s41598-018-25842-610.1038/s41598-018-25842-6595179329760397
  11. [11] X. Xie, Y. Li, M. Zhang, and L. Shen, “Robust segmentation of nucleus in histopathology images via mask R-CNN,” Springer, pp. 428–436, Jan. 2019. https://doi.org/10.1007/978-3-030-11723-8_4310.1007/978-3-030-11723-8_43
  12. [12] Y. Ren, J. Ma, J. Xiong, Y. Chen, L. Lu, and J. Zhao, “Improved false positive reduction by novel morphological features for computer-aided polyp detection in CT colonography,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 1, pp. 324–333, Jan. 2019. https://doi.org/10.1109/JBHI.2018.280819910.1109/JBHI.2018.280819929994459
  13. [13] Q. Dou et al., “3D deeply supervised network for automated segmentation of volumetric medical images,” Medical Image Analysis, vol. 41, pp. 40–54, Oct. 2017. https://doi.org/10.1016/j.media.2017.05.00110.1016/j.media.2017.05.00128526212
  14. [14] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015. Lecture Notes in Computer Science, Springer, Champ, vol 935, pp. 234–241, Nov. 2015. https://doi.org/10.1007/978-3-319-24574-4_2810.1007/978-3-319-24574-4_28
  15. [15] X. Zhou, T. Ito, and R. Takayama, “Three-dimensional CT image segmentation by combining 2D fully convolutional network with 3D majority voting,” in Deep Learning and Data Labeling for Medical Applications, DLMIA 2016. Lecture Notes in Computer Science, Springer, Cham, vol. 10008, pp. 111–120, Sep. 2016. https://doi.org/10.1007/978-3-319-46976-8_1210.1007/978-3-319-46976-8_12
  16. [16] M. Havaei et al., “Brain tumour segmentation with deep neural networks,” Medical Image Analysis, vol. 35, pp. 18–31, Jan. 2017. https://doi.org/10.1016/j.media.2016.05.00410.1016/j.media.2016.05.00427310171
  17. [17] H. R. Roth, L. Lu, N. Lay, A. P. Harrison, A. Farag, A. Sohn, and R. M. Summers, “Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localisation and segmentation,” Medical Image Analysis, vol. 45, pp 94–107, Apr. 2018. https://doi.org/10.1016/j.media.2018.01.00610.1016/j.media.2018.01.00629427897
  18. [18] E. Trivizakis et al., “Extending 2-D convolutional neural networks to 3-D for advancing deep learning cancer classification with application to MRI liver tumor differentiation,” IEEE J. Biomed. Heal. Informatics, vol. 23, no. 3, pp. 923–930, May 2019. https://doi.org/10.1109/JBHI.2018.288627610.1109/JBHI.2018.288627630561355
  19. [19] A. Sinha and J. Dolz, “Multi-scale guided attention for medical image segmentation,” arXiv:1906.02849 [cs.CV], Jun. 2019.
  20. [20] Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-net: Learning dense volumetric segmentation from sparse annotation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9901 LNCS, pp. 424–432, Oct. 2016. https://doi.org/10.1007/978-3-319-46723-8_4910.1007/978-3-319-46723-8_49
  21. [21] F. Milletari, N. Navab, and S. Ahmadi, “V-Net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571, Dec. 2016. https://doi.org/10.1109/3DV.2016.7910.1109/3DV.2016.79
  22. [22] W. Zhu et al., “AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy,” The International Journal of Medical Physics and Practice, vol. 46, no. 2, pp. 576–589, Nov. 2018. http://dx.doi.org/10.1002/mp.1330010.1002/mp.13300
  23. [23] H. Chen, Q. Dou, L. Yu, J. Qin, and P.-A. Heng, “VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images,” NeuroImage, vol. 170, pp. 446–455, Apr. 2018. https://doi.org/10.1016/j.neuroimage.2017.04.04110.1016/j.neuroimage.2017.04.041
  24. [24] H. R. Roth et al., “An application of cascaded 3D fully convolutional networks for medical image segmentation,” Computerized Medical Imaging Graphics, vol. 66, pp. 90–99, Jun. 2018. https://doi.org/10.1016/j.compmedimag.2018.03.00110.1016/j.compmedimag.2018.03.001
  25. [25] V. V. Romanuke, “An attempt of finding an appropriate number of convolutional layers in CNNs based on benchmarks of heterogeneous datasets,” Electrical, Control and Communication Engineering, vol. 14, no. 1, pp. 51–57, Jul. 2018. https://doi.org/10.2478/ecce-2018-000610.2478/ecce-2018-0006
  26. [26] V. V. Romanuke, “Appropriate number and allocation of ReLUs in convolutional neural networks,” Research Bulletin of the National Technical University of Ukraine “Kyiv Polytechnic Institute”, no. 1, pp. 69–78, 2017. https://doi.org/10.20535/1810-0546.2017.1.8815610.20535/1810-0546.2017.1.88156
  27. [27] V. V. Romanuke, “Appropriate number of standard 2×2 Max Pooling layers and their allocation in convolutional neural networks for diverse and heterogeneous datasets,” Information Technology and Management Science, vol. 20, no. 1, pp. 12–19, Jan. 2018. https://doi.org/10.1515/itms-2017-000210.1515/itms-2017-0002
  28. [28] P. M. Radiuk, “Impact of training set batch size on the performance of convolutional neural networks for diverse datasets,” Information Technology and Management Science, vol. 20, no. 1, pp. 20–24, Jan. 2017. https://doi.org/10.1515/itms-2017-000310.1515/itms-2017-0003
  29. [29] The Cancer Imaging Archive, “TCIA Collections”. [Online]. Available: https://www.cancerimagingarchive.net/#collections-list. [Accessed: Feb. 11, 2019].
  30. [30] K. H. Zou, S. K. Warfield, A. Bharatha, C. M. C. Tempany M. R. Kaus, et al., “Statistical validation of image segmentation quality based on a spatial overlap index,” Academic Radiology, vol. 11, no. 2, pp. 178–189, Feb. 2004. https://doi.org/10.1016/S1076-6332(03)00671-810.1016/S1076-6332(03)00671-8
  31. [31] Q. Huang, J. Sun, H. Ding, X. Wang, and G. Wang, “Robust liver vessel extraction using 3D U-Net with variant dice loss function,” Computers in Biology and Medicine, vol. 101, pp. 153–162, Oct. 2018. https://doi.org/10.1016/j.compbiomed.2018.08.01810.1016/j.compbiomed.2018.08.01830144657
  32. [32] M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ‘16), pp. 265–283, Nov. 2016. [Online]. Available: https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi
  33. [33] P. Radiuk, “Applying 3D U-Net architecture to the task of multi-organ segmentation in computed tomography,” GitHub, Inc., Feb. 2020. [Online]. Available: https://github.com/soolstafir/3D-U-Net-in-CT [Accessed: Mar. 01, 2020].10.2478/acss-2020-0005
DOI: https://doi.org/10.2478/acss-2020-0005 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683
Language: English
Page range: 43 - 50
Published on: Jun 5, 2020
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2020 Pavlo Radiuk, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.