Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images

Julia M.H. Noothout*, Bob D. De Vos, Jelmer M. Wolterink, Elbrich M. Postma, Paul A.M. Smeets, Richard A.P. Takx, Tim Leiner, Max A. Viergever, Ivana Išgum

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

76 Citations (Scopus)
840 Downloads (Pure)


In this study, we propose a fast and accurate method to automatically localize anatomical landmarks in medical images. We employ a global-to-local localization approach using fully convolutional neural networks (FCNNs). First, a global FCNN localizes multiple landmarks through the analysis of image patches, performing regression and classification simultaneously. In regression, displacement vectors pointing from the center of image patches towards landmark locations are determined. In classification, presence of landmarks of interest in the patch is established. Global landmark locations are obtained by averaging the predicted displacement vectors, where the contribution of each displacement vector is weighted by the posterior classification probability of the patch that it is pointing from. Subsequently, for each landmark localized with global localization, local analysis is performed. Specialized FCNNs refine the global landmark locations by analyzing local sub-images in a similar manner, i.e. by performing regression and classification simultaneously and combining the results. Evaluation was performed through localization of 8 anatomical landmarks in CCTA scans, 2 landmarks in olfactory MR scans, and 19 landmarks in cephalometric X-rays. We demonstrate that the method performs similarly to a second observer and is able to localize landmarks in a diverse set of medical images, differing in image modality, image dimensionality, and anatomical coverage.

Original languageEnglish
Article number9139480
Pages (from-to)4011-4022
Number of pages12
JournalIEEE transactions on medical imaging
Issue number12
Early online date13 Jul 2020
Publication statusPublished - Dec 2020


  • cardiac CT
  • cephalometric X-ray
  • classification
  • convolutional neural network
  • deep learning
  • Landmark localization
  • olfactory MR
  • regression
  • 22/2 OA procedure


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