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)


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.


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.


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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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