Details

PDF BIBTEX RIS

Title

Segmentation of bone structures with the use of deep learning techniques

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2021

Volume

69

Issue

3

Authors

Krawczyk, Zuzanna ; Starzyński, Jacek

Affiliation

Krawczyk, Zuzanna : Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, Poland ; Starzyński, Jacek : Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, Poland

Keywords

deep learning ; semantic segmentation ; U-net ; FCN ; ResNet ; computed tomography

Divisions of PAS

Nauki Techniczne

Coverage

e136751

Bibliography

  1.  E. Stindel, et al., “Bone morphing: 3D morphological data for total knee arthroplasty” Comput. Aided Surg. 7(3), 156–168 (2002), doi: 10.1002/igs.10042.
  2.  F. Azimifar, K. Hassani, A.H. Saveh, and F.T. Ghomsheh, “A medium invasiveness multi-level patient’s specific template for pedicle screw placement in the scoliosis surgery”, Biomed. Eng. Online 16, 130 (2017), doi: 10.1186/s12938-017-0421-0.
  3.  L. Yahia-Cherif, B. Gilles, T. Molet, and N. Magnenat-Thalmann, “Motion capture and visualization of the hip joint with dynamic MRI and optical systems”, Comp. Anim. Virtual Worlds 15, 377–385 (2004).
  4.  V. Pekar, T.R. McNutt, and M.R. Kaus, “Automated modelbased organ delineation for radiotherapy planning in prostatic region”, Int. J. Radiat. Oncol. Biol. Phys. 60(3), 973–980 (2004).
  5.  D. Ravì, et al., “Deep learning for health informatics,” IEEE J. Biomed. Health. Inf. 21(1), 4–21 (2017), doi: 10.1109/JBHI.2016.2636665.
  6.  G. Litjens, et al., “A survey on deep learning in medical image analysis”, Med. Image Anal. 42, 60–88 (2017), doi: 10.1016/j. media.2017.07.005.
  7.  Z. Krawczyk and J. Starzyński, “YOLO and morphingbased method for 3D individualised bone model creation”, 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom (2020), doi: 10.1109/IJCNN48605.2020.9206783.
  8.  J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 3431–3440 (2015), doi: 10.1109/CVPR.2015.7298965.
  9.  H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid Scene Parsing Network,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 6230–6239 (2017), doi: 10.1109/CVPR.2017.660.
  10.  O. Ronneberger, P. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation”, in Navab N., Hornegger J., Wells W., Frangi A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol. 9351, Springer, Cham. (2015), doi: 10.1007/978-3-319-24574-4_28.
  11.  V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A deep convolutional encoder-decoder architecture for image segmentation”, IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017), doi: 10.1109/TPAMI.2016.2644615.
  12.  Z. Krawczyk and J. Starzyński, “Deep learning approach for bone model creation”, 2020 IEEE 21st International Conference on Computational Problems of Electrical Engineering (CPEE), (2020), doi: 10.1109/CPEE50798.2020.9238678.
  13.  W. Qin, J. Wu, F. Han, Y. Yuan, W. Zhao, B. Ibragimov, J. Gu, and L. Xing, “Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation”, Phys. Med. Biol. 63(9), 95017 (2018), doi: 10.1088/1361‒6560/aabd19.
  14.  S. Nikolov, et al., “Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy”, Technical Report, ArXiv, (2018), doi: arXiv:1809.04430.
  15.  T.L. Kline, et al., “Performance of an artificial multi-observer deep neural network for fully automated segmentation of polycystic kidneys”, J Digit Imaging 30, 442–448 (2017), doi: 10.1007/s10278-017-9978-1.
  16.  A. Wadhwa, A. Bhardwaj, and V.S. Verma, “A review on brain tumor segmentation of MRI images”, Magn. Reson. Imaging 61, 247–259 (2019), doi: 10.1016/j.mri.2019.05.043.
  17.  J. Xu, X. Luo, G. Wang, H. Gilmore, and A. Madabhushi, “A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images”, Neurocomputing 191, 214–223 (2016), doi: 10.1016/j.neucom.2016.01.034.
  18.  Z. Swiderska-Chadaj, T. Markiewicz, J. Gallego, G. Bueno, B. Grala, and M. Lorent, “Deep learning for damaged tissue detection and segmentationin Ki-67 brain tumor specimens based on the U-net model”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 849–856 (2018), doi: 10.24425/bpas.2018.125932.
  19.  S. Lindgren Belal, et. al., “Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CTbased 3D quantification of skeletal metastases”, Eur. J. Radiol. 113, 89–95 (2019), doi: 10.1016/j.ejrad.2019.01.028.
  20.  A. Klein, J. Warszawski, J. Hillengaß, and K.H. Maier-Hein, “Automatic bone segmentation in whole-body CT images”, Int J Comput Assist Radiol Surg. 14(1), 21–29 (2019), doi: 10.1007/s11548-018-1883-7.
  21.  J. Minnema, M. van Eijnatten, W. Kouw, F. Diblen, A. Mendrik, and J. Wolff, “CT image segmentation of bone for medical additive manufacturing using a convolutional neural network”, Comput. Biol. Med. 103, 130–139 (2018), https://doi.org/10.1016/j. compbiomed.2018.10.012.
  22.  T. Les, T. Markiewicz, T. Osowski, and M. Jesiotr, “Automatic reconstruction of overlapped cells in breast cancer FISH images”, Expert Syst. Appl. 137, 335–342 (2019).
  23.  F. Yokota, T. Okada, M. Takao, N. Sugano, Y. Tada, and Y. Sato, “Automated segmentation of the femur and pelvis from 3D CT data of diseased hip using hierarchical statistical shape model of joint structure”, Med Image Comput Comput Assist Interv., 811–818 (2019), doi: 10.1007/978-3-642-04271-3_98.
  24.  D. Gupta, “Semantic segmentation library”, accessed 19-Jan-202, [Online], Available: https: //divamgupta.com/image- segmentation/2019/06/06/ deep-learning-semantic-segmentation-keras.html.
  25.  A.B. Jung, et al., “Imgaug library”, accessed 01-Feb-2020, [Online], Available: https://github.com/aleju/imgaug (2020).
  26.  F. Chollet, et al., “Keras”, [Online], Available: https://keras.io, (2015).
  27.  M. Abadi, et al., “TensorFlow: Large-scale machine learning on heterogeneous systems”, [Online], Available: tensorflow.org, (2015).
  28.  K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition”, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 770–778 (2016), doi: 10.1109/CVPR.2016.90.
  29.  K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition”, CoRR, (2015).
  30.  O. Russakovsky, et al., “ImageNet large scale visual recognition challenge”, Int. J. Comput. Vision 115(3), 211–252 (2015), doi: 10.1007/ s11263-015-0816-y.
  31.  VGG network weights, [Online], Available: https://www.robots.ox.ac.uk/~vgg/research/very_deep/
  32.  Resnet network weights, [Online], Available: https://github.com/KaimingHe/deep-residual-networks.
  33.  P. Leydon, M. O’Connell, D. Greene, K. M. Curran, “Bone Segmentation in Contrast Enhanced Whole-Body Computed Tomography”, arXiv (2020), https://arxiv.org/abs/2008.05223.

Date

10.03.2021

Type

Article

Identifier

DOI: 10.24425/bpasts.2021.136751

Source

Bulletin of the Polish Academy of Sciences: Technical Sciences; Early Access; e136751
×