Accès libre

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

   | 05 juin 2020
À propos de cet article

Citez

[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-071516-044442547972228301734Search in Google Scholar

[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.2016.12.038Search in Google Scholar

[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.2016.257268327244717Search in Google Scholar

[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.2014.06.077416584225042445Search in Google Scholar

[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.2018.03.00629679847Search in Google Scholar

[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.2019.06.00931395244Search in Google Scholar

[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/srep24454483219927079888Search in Google Scholar

[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/s10120-018-0793-229335825Search in Google Scholar

[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.2018.07.002674251030007253Search in Google Scholar

[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/s41598-018-25842-6595179329760397Search in Google Scholar

[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_43Search in Google Scholar

[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.2018.280819929994459Search in Google Scholar

[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.2017.05.00128526212Search in Google Scholar

[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_28Search in Google Scholar

[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_12Search in Google Scholar

[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.2016.05.00427310171Search in Google Scholar

[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.2018.01.00629427897Search in Google Scholar

[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.2018.288627630561355Search in Google Scholar

[19] A. Sinha and J. Dolz, “Multi-scale guided attention for medical image segmentation,” arXiv:1906.02849 [cs.CV], Jun. 2019.Search in Google Scholar

[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_49Search in Google Scholar

[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.2016.79Search in Google Scholar

[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.13300Search in Google Scholar

[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.2017.04.041Search in Google Scholar

[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.2018.03.001Search in Google Scholar

[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-2018-0006Search in Google Scholar

[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/10.20535/1810-0546.2017.1.8815610.20535/1810-0546.2017.1.88156Search in Google Scholar

[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-2017-0002Search in Google Scholar

[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-2017-0003Search in Google Scholar

[29] The Cancer Imaging Archive, “TCIA Collections”. [Online]. Available: https://www.cancerimagingarchive.net/#collections-list. [Accessed: Feb. 11, 2019].Search in Google Scholar

[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)00671-8Search in Google Scholar

[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.2018.08.01830144657Search in Google Scholar

[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/abadiSearch in Google Scholar

[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-2020-0005Search in Google Scholar

eISSN:
2255-8691
Langue:
Anglais