Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs

TL;DR

This paper introduces a multi-stage cascaded CNN approach with shape constraints to accurately localize vertebral landmarks in X-ray images, improving upon previous methods by reducing localization error.

Contribution

The paper presents a novel multi-stage cascaded CNN framework with PCA-based shape constraints for precise vertebral landmark localization in spinal X-ray images.

Findings

Achieved lower relative error in landmark localization compared to state-of-the-art methods.

Effectively separated center and corner point localization to improve accuracy.

Demonstrated robustness on a dataset of 609 spinal X-ray images.

Abstract

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.