Automated anatomical landmark detection ondistal femur surface using convolutional neural network

Dong Yang, Shaoting Zhang, Zhennan Yan, Chaowei Tan, Kang Li, Dimitris Metaxas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

38 Scopus citations

Abstract

Accurate localization of the anatomical landmarks on distal femur bone in the 3D medical images is very important for knee surgery planning and biomechanics analysis. However, the landmark identification process is often conducted manually or by using the inserted auxiliaries, which is time-consuming and lacks of accuracy. In this paper, an automatic localization method is proposed to determine positions of initial geometric landmarks on femur surface in the 3D MR images. Based on the results from the convolutional neural network (CNN) classifiers and shape statistics, we use the narrow-band graph cut optimization to achieve the 3D segmentation of femur surface. Finally, the anatomical landmarks are located on the femur according to the geometric cues of surface mesh. Experiments demonstrate that the proposed method is effective, efficient, and reliable to segment femur and locate the anatomical landmarks.

Original languageEnglish (US)
Title of host publication2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PublisherIEEE Computer Society
Pages17-21
Number of pages5
ISBN (Electronic)9781479923748
DOIs
StatePublished - Jul 21 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2015-July
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States
CityBrooklyn
Period4/16/154/19/15

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Keywords

  • Deep learning
  • anatomical landmark detection
  • convolutional neural network
  • graph cut
  • mesh curvature

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