Spinal Cord Centerline Detection: OptiC

This page presents our work on spinal cord centerline localization.

Our main result is a very robust and efficient method to localize the spinal cord on 3D images.

A localisation map is first computed using a HOG+SVM spinal cord detector. The centerline curve is then found as the solution of a nonlinear optimization problem. An original algorithm is proposed to find the global solution of this problem in a efficient and non iterative manner.

The method has been presented at RITS 2017[1],  MICCAI 2017 [2] and has been published in Medical Image Analysis[3].

Details of the method can be found here.

 

 

Software

The binaries are availaible here for linux-x86_64, macos 10.7, macos 10.11 and windows.

It is also included and packaged in the Spinal Cord Toolbox as sct_detect_spinalcord along with a lot of spinal cord dedicated tools. It is now used for example to initialize the spinal cord segmentation tool.

Please reference the MEDIA journal article [3].

 

Author

M. Sdika

Collaborations

V. Callot at CRMBM

The Neuropoly team at the Polytechnique Montréal.

 

 

 

 

 

References

  1. SDIK-17. Sdika M, Callot V. Automatic Detection of the Spinal Cord Centerline using Machine Learning and Global Nonlinear Optimization. Dans: Recherche en Imagerie et Technologies pour la Santé (RITS). Recherche en Imagerie et Technologies pour la Santé (RITS). ; 2017.
  2. GROS-17a. Gros C, De Leener B, Dupont SM, Martin AR, Fehlings MG, Bakshi R, Tummala S, Auclair V, McLaren DG, Callot V, et al. OptiC: Robust and Automatic Spinal Cord Localization on a Large Variety of MRI Data Using a Distance Transform Based Global Optimization. Dans: International Conference on Medical Image Computing and Computer-Assisted Intervention. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2017. p. 712–719.
  3. GROS-18. Gros C, De Leener B, Dupont SM, Martin AR, Fehlings MG, Bakshi R, Tummala S, Auclair V, McLaren DG, Callot V, et al. Automatic spinal cord localization, robust to MRI contrasts using global curve optimization. Med Image Anal. 2018 ;44:215-227.