Kavli Affiliate: Li Xin Li
| First 5 Authors: Zhiwei Wang, Jinxin Lv, Yunqiao Yang, Yuanhuai Liang, Yi Lin
| Summary:
Vertebral landmark localization is a crucial step for variant spine-related
clinical applications, which requires detecting the corner points of 17
vertebrae. However, the neighbor landmarks often disturb each other for the
homogeneous appearance of vertebrae, which makes vertebral landmark
localization extremely difficult. In this paper, we propose multi-stage
cascaded convolutional neural networks (CNNs) to split the single task into two
sequential steps, i.e., center point localization to roughly locate 17 center
points of vertebrae, and corner point localization to find 4 corner points for
each vertebra without distracted by others. Landmarks in each step are located
gradually from a set of initialized points by regressing offsets via cascaded
CNNs. Principal Component Analysis (PCA) is employed to preserve a shape
constraint in offset regression to resist the mutual attraction of vertebrae.
We evaluate our method on the AASCE dataset that consists of 609 tight spinal
anterior-posterior X-ray images and each image contains 17 vertebrae composed
of the thoracic and lumbar spine for spinal shape characterization.
Experimental results demonstrate our superior performance of vertebral landmark
localization over other state-of-the-arts with the relative error decreasing
from 3.2e-3 to 7.2e-4.
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