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Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network

  • Adhish Prasoon
  • Kersten Petersen
  • Christian Igel
  • François Lauze
  • Erik Dam
  • Mads Nielsen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

Segmentation of anatomical structures in medical images is often based on a voxel/pixel classification approach. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images that fosters categorization. We propose a novel system for voxel classification integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D image, respectively. We applied our method to the segmentation of tibial cartilage in low field knee MRI scans and tested it on 114 unseen scans. Although our method uses only 2D features at a single scale, it performs better than a state-of-the-art method using 3D multi-scale features. In the latter approach, the features and the classifier have been carefully adapted to the problem at hand. That we were able to get better results by a deep learning architecture that autonomously learns the features from the images is the main insight of this study.

Keywords

Articular Cartilage Convolutional Neural Network Tibial Cartilage Convolutional Layer Training Data Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  2. 2.
    Schulz, H., Behnke, S.: Learning Object-Class Segmentation with Convolutional Neural Networks. In: ESANN (2012)Google Scholar
  3. 3.
    Turaga, S.C., Murray, J.F., Jain, V., Roth, F., Helmstaedter, M., Briggman, K.L., Denk, W., Seung, H.S.: Convolutional Networks can Learn to Generate Affinity Graphs for Image Segmentation. Neural Computation 22(2), 511–538 (2010)CrossRefzbMATHGoogle Scholar
  4. 4.
    Jackson, D., Simon, T., Aberman, H.: Symptomatic Articular Cartilage Degeneration: The impact in the New Millenium. Clinical Orthopaedics and Related Research 133, 14–25 (2001)CrossRefGoogle Scholar
  5. 5.
    Folkesson, J., Dam, E., Olsen, O., Pettersen, P., Christiansen, C.: Segmenting Articular Cartilage Automatically Using a Voxel Classification Approach. IEEE TMI 26(1), 106–115 (2007)Google Scholar
  6. 6.
    Solloway, S., Hutchinson, C., Vaterton, J., Taylor, C.: The Use of Active Shape Models for Making Thickness Measurements of Articular Cartilage from MR Images. Magnetic Resonance in Medicine 37, 943–952 (1997)CrossRefGoogle Scholar
  7. 7.
    Pakin, S.K., Tamez-Pena, J.G., Totterman, S., Parker, K.J.: Segmentation, Surface Extraction and Thickness Computation of Articular Cartilage. In: SPIE Medical Imaging, vol. 4684, pp. 155–166 (2002)Google Scholar
  8. 8.
    Fripp, J., Crozier, S., Warfield, S., Ourselin, S.: Automatic Segmentation and Quantitative Analysis of the Articular Cartilages from Magnetic Resonance Images of the Knee. IEEE TMI 29(1), 55–64 (2010)Google Scholar
  9. 9.
    Yin, Y., Xiangmin, Z., Williams, R., Xiaodong, W., Anderson, D., Sonka, M.: LOGISMOS - Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces: Cartilage Segmentation in the Knee Joint. IEEE TMI 29(12), 2023–2037 (2010)Google Scholar
  10. 10.
    Ning, F., Delhomme, D., LeCun, Y., Piano, F., Bottou, L., Barbano, P.E.: Toward Automatic Phenotyping of Developing Embryos from Videos. IEEE TIP 14(9), 1360–1371 (2005)Google Scholar
  11. 11.
    Sermanet, P., LeCun, Y.: Traffic Sign Recognition with Multi-Scale Convolutional Networks. In: IJCNN, pp. 2809–2813 (2011)Google Scholar
  12. 12.
    Ciresan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: Flexible, High Performance Convolutional Neural Networks for Image Classification. In: IJCAI, 1237–1242 (2011)Google Scholar
  13. 13.
    Wang, T., Wu, D.J., Coates, A., Ng, A.Y.: End-to-End Text Recognition with Convolutional Neural Networks. In: ICPR, pp. 3304–3308 (2012)Google Scholar
  14. 14.
    Ji, S., Xu, W., Yang, M., Yu, K.: 3D Convolutional Neural Networks for Human Action Recognition. IEEE TPAMI 35(1), 221–231 (2013)CrossRefGoogle Scholar
  15. 15.
    Le, Q.V., Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., Ng, A.Y.: On Optimization Methods for Deep Learning. In: ICML, 265–272 (2011)Google Scholar
  16. 16.
    Simard, P.Y., Steinkraus, D., Platt, J.C.: Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis. In: ICDAR, pp. 958–962 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Adhish Prasoon
    • 1
  • Kersten Petersen
    • 1
  • Christian Igel
    • 1
  • François Lauze
    • 1
  • Erik Dam
    • 2
  • Mads Nielsen
    • 1
    • 2
  1. 1.Department of Computer ScienceUniversity of CopenhagenDenmark
  2. 2.BiomediqDenmark

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