Fast and Robust Detection of Anatomical Landmarks Using Cascaded 3D Convolutional Networks Guided by Linear Square Regression

  • Zi-Rui WangEmail author
  • Bao-Cai Yin
  • Jun Du
  • Cong Liu
  • Xiaodong Tao
  • Guoping Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)


Detecting anatomical landmarks on structural magnetic resonance imaging (MRI) is an important medical computer-aid technique. However, for some brain anatomical landmarks detection, linear/non-linear registration with skull stripping across subjects is usually unavoidable. In this paper, we propose a novel method. Starting from the original MRI data, a series of 3D convolutional neural networks (cascaded 3D-CNNs) are adopted to iteratively update the predicted landmarks. Specially, the predicted landmarks of each 3D-CNN model are used to estimate the corresponding linear transformation matrix by linear square regression, which is very different from traditional registration methods. Based on the estimated matrix, we can use it to transform the original image for getting the new image for the next 3D-CNN model. With these cascaded 3D-CNNs and linear square regression, we can finally achieve registration and landmark detection.


Anatomical landmark detection Cascaded 3D-CNNs Linear square regression Fast Robust 



This work was supported in part by the National Key R&D Program of China under contract No. 2017YFB1002202, in part by the National Natural Science Foundation of China under Grants 61671422 and U1613211, in part by the MOE-Microsoft Key Laboratory of USTC. The authors would like to thank Dr. Dinggang Shen for the contributions on implementation.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zi-Rui Wang
    • 1
    Email author
  • Bao-Cai Yin
    • 2
  • Jun Du
    • 1
  • Cong Liu
    • 2
  • Xiaodong Tao
    • 2
  • Guoping Hu
    • 2
  1. 1.NELSLIPUniversity of Science and Technology of ChinaHefeiChina
  2. 2.iFLYTEK AI ResearchHefeiChina

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