Abstract
Precise vertebral segmentation provides the basis for spinal image analyses and interventions, such as vertebral compression fracture detection and other abnormalities. Deep learning is a popular and useful paradigm for medical image process. In this paper, we proposed an iterative vertebrae instance segmentation model, which has good generalization ability for segmenting all types of vertebrae, including cervical, thoracic, and lumbar vertebrae. In experimental results, our model not only used 17% less memory but also achieves better performance on vertebrae segmentation compared to existing methods. The existing method provides only two output for segmentation and classification respectively. However, with more memory available, our model is capable of providing third output for accurate anatomical prediction under the same amount of memory.
Highlights
The main trunk in the human body is composed of vertebrae, which are adjacent to the critical nerve system
Traditional vertebral segmentation primarily uses mathematical methods to analyze cone features [8], [9], [13] or different model-based for addressing model fitting problems [1], [2], [6]
Darwish et al [4] integrated and detailed automatic segmen- tation techniques, including conventional- based, anatomical model-based and random forest-based, to identify and lo- cate individual vertebrae in traditional spines. Most of these traditional vertebral segmentation techniques mathematically analyze the characteristics of the threshold, edge and verte- bral morphology based on a 2D spine cut, and are applied to specific targets or shapes
Summary
The main trunk in the human body is composed of vertebrae, which are adjacent to the critical nerve system. Many diseases, such as spinal spondylolisthesis, compression fractures, disc herniation, spurs, etc. Darwish et al [4] integrated and detailed automatic segmen- tation techniques, including conventional- based, anatomical model-based and random forest-based, to identify and lo- cate individual vertebrae in traditional spines. Most of these traditional vertebral segmentation techniques mathematically analyze the characteristics of the threshold, edge and verte- bral morphology based on a 2D spine cut, and are applied to specific targets or shapes.
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