Automatic 3D cephalometric annotation system using shadowed 2D image-based machine learning

Sung Min Lee, Hwa Pyung Kim, Kiwan Jeon, Sang Hwy Lee, Jin Keun Seo

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


This paper presents a new approach to automatic three-dimensional (3D) cephalometric annotation for diagnosis, surgical planning, and treatment evaluation. There has long been considerable demand for automated cephalometric landmarking, since manual landmarking requires considerable time and experience as well as objectivity and scrupulous error avoidance. Due to the inherent limitation of two-dimensional (2D) cephalometry and the 3D nature of surgical simulation, there is a trend away from current 2D to 3D cephalometry. Deep learning approaches to cephalometric landmarking seem highly promising, but there exist serious difficulties in handling high dimensional 3D CT data, dimension referring to the number of voxels. To address this issue of dimensionality, this paper proposes a shadowed 2D image-based machine learning method which uses multiple shadowed 2D images with various lighting and view directions to capture 3D geometric cues. The proposed method using VGG-net was trained and tested using 2700 shadowed 2D images and corresponding manual landmarkings. Test data evaluation shows that our method achieved an average point-to-point error of 1.5 mm for the seven major landmarks.

Original languageEnglish
Article number055002
JournalPhysics in medicine and biology
Issue number5
Publication statusPublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 Institute of Physics and Engineering in Medicine.

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Automatic 3D cephalometric annotation system using shadowed 2D image-based machine learning'. Together they form a unique fingerprint.

Cite this