Multi-object Model-Based Multi-atlas Segmentation Constrained Grid Cut for Automatic Segmentation of Lumbar Vertebrae from CT Images

  • Weimin Yu
  • Wenyong Liu
  • Liwen Tan
  • Shaoxiang Zhang
  • Guoyan ZhengEmail author
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1093)


In this chapter, we present a multi-object model-based multi-atlas segmentation constrained grid cut method for automatic segmentation of lumbar vertebrae from a given lumbar spinal CT image. More specifically, our automatic lumbar vertebrae segmentation method consists of two steps: affine atlas-target registration-based label fusion and bone-sheetness assisted multi-label grid cut which has the inherent advantage of automatic separation of the five lumbar vertebrae from each other. We evaluate our method on 21 clinical lumbar spinal CT images with the associated manual segmentation and conduct a leave-one-out study. Our method achieved an average Dice coefficient of 93.9 ± 1.0% and an average symmetric surface distance of 0.41 ± 0.08 mm.


Multi-object model-based multi-atlas segmentation Grid cut CT Lumbar vertebrae Affine registration Label fusion Image-guided spinal surgery 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Weimin Yu
    • 1
  • Wenyong Liu
    • 2
    • 3
  • Liwen Tan
    • 4
  • Shaoxiang Zhang
    • 4
  • Guoyan Zheng
    • 1
    Email author
  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland
  2. 2.School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
  3. 3.Beihang Advanced Innovation Centre for Biomedical EngineeringBeihang UniversityBeijingChina
  4. 4.The Institute of Digital MedicineThird Military Medical UniversityChongqingChina

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