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Vertebral body segmentation with GrowCut: Initial experience, workflow and practical application.

Jan Egger^1,2,3,4, Christopher Nimsky³, Xiaojun Chen⁵

¹Institute of Computer Graphics and Vision, Graz University of Technology (TUG), Graz, Austria.

SAGE Open Medicine

|November 23, 2017

Summary

This summary is machine-generated.

The GrowCut algorithm significantly reduces vertebral body segmentation time compared to manual methods. This cellular automata approach offers a faster and potentially more accurate alternative for spinal imaging analysis.

Keywords:

Dice Score GrowCut Segmentation magnetic resonance imaging vertebral body

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Area of Science:

Medical Imaging
Computational Anatomy
Biomedical Engineering

Background:

Spinal diseases necessitate accurate diagnosis and treatment, with rising surgical interventions due to an aging population.
Manual segmentation of vertebral bodies from medical images is time-consuming and prone to interpretation errors.
Automated segmentation methods are crucial for improving diagnostic efficiency and accuracy in spinal imaging.

Purpose of the Study:

To evaluate the efficacy of a cellular automata-based approach, specifically the GrowCut algorithm, for segmenting vertebral bodies.
To compare the time efficiency and accuracy of GrowCut segmentation against manual, slice-by-slice segmentation.
To determine if GrowCut offers a viable alternative for reducing interpretation errors and saving time in spinal imaging analysis.

Main Methods:

T2-weighted magnetic resonance imaging (MRI) of the spine was acquired.
Ground truth segmentation was established through slice-by-slice manual segmentation by multiple neurosurgeons.
The GrowCut algorithm was applied by a physician for automated segmentation of the same vertebral bodies.

Main Results:

GrowCut segmentation achieved a Dice Score of 82.99% ± 5.03% and a Hausdorff distance of 18.91 ± 7.2 voxels when compared to manual segmentation.
The average segmentation time using GrowCut was significantly reduced to 5.77 ± 0.73 minutes per slice.
Algorithmic segmentation demonstrated a substantial decrease in time compared to manual slice-by-slice procedures.

Conclusions:

The GrowCut algorithm, available in 3D Slicer, was successfully applied for 3D vertebral body segmentation.
This study represents the first investigation into the use of the GrowCut method for vertebral body segmentation.
GrowCut offers a promising alternative to manual segmentation, providing consistent time savings and comparable accuracy for vertebral body analysis.