ConvNet-Based Localization of Anatomical Structures in 3-D Medical Images

Bob D. de Vos*, Jelmer M. Wolterink, Pim A. de Jong, Tim Leiner, Max A. Viergever, Ivana Isgum

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

74 Citations (Scopus)
82 Downloads (Pure)


Localization of anatomical structures is a prerequisite for many tasks in a medical image analysis. We propose a method for automatic localization of one or more anatomical structures in 3-D medical images through detection of their presence in 2-D image slices using a convolutional neural network (ConvNet). A single ConvNet is trained to detect the presence of the anatomical structure of interest in axial, coronal, and sagittal slices extracted from a 3-D image. To allow the ConvNet to analyze slices of different sizes, spatial pyramid pooling is applied. After detection, 3-D bounding boxes are created by combining the output of the ConvNet in all slices. In the experiments, 200 chest CT, 100 cardiac CT angiography (CTA), and 100 abdomen CT scans were used. The heart, ascending aorta, aortic arch, and descending aorta were localized in chest CT scans, the left cardiac ventricle in cardiac CTA scans, and the liver in abdomen CT scans. Localization was evaluated using the distances between automatically and manually defined reference bounding box centroids and walls. The best results were achieved in the localization of structures with clearly defined boundaries (e.g., aortic arch) and the worst when the structure boundary was not clearly visible (e.g., liver). The method was more robust and accurate in localization multiple structures.

Original languageEnglish
Article number7862905
Pages (from-to)1470-1481
Number of pages12
JournalIEEE transactions on medical imaging
Issue number7
Publication statusPublished - Jul 2017
Externally publishedYes


  • Convolutional neural networks
  • CT
  • Deep learning
  • Detection
  • Localization


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