title: Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network.
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.
authors.affiliation: Department of Computer Science, University of Copenhagen, Denmark.
authors.forename: Adhish
authors.initials: A
authors.lastname: Prasoon
authors.affiliation: Department of Computer Science, University of Copenhagen, Denmark.
authors.forename: Kersten
authors.initials: K
authors.lastname: Petersen
authors.affiliation: Department of Computer Science, University of Copenhagen, Denmark.
authors.forename: Christian
authors.initials: C
authors.lastname: Igel
authors.affiliation: Department of Computer Science, University of Copenhagen, Denmark.
authors.forename: François
authors.initials: F
authors.lastname: Lauze
authors.affiliation: Biomediq, Denmark.
authors.forename: Erik
authors.initials: E
authors.lastname: Dam
authors.affiliation: Department of Computer Science, University of Copenhagen, Denmark.
authors.forename: Mads
authors.initials: M
authors.lastname: Nielsen
mesh_terms: D000465:Algorithms; D002358:Cartilage, Articular / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007719:Knee Joint / Q000033:anatomy & histology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity