SLD: Segmentation-Based Landmark Detection for Spinal Ligaments

TL;DR

This paper introduces a novel segmentation-based method for accurately detecting spinal ligament landmarks across all spinal regions, improving biomechanical modeling of the spine.

Contribution

The work presents a new shape-based segmentation and rule-based approach that outperforms existing methods in landmark detection accuracy and generalization.

Findings

Achieved a mean absolute error of 0.7 mm in landmark detection

Demonstrated strong generalization across multiple spinal datasets

Outperformed existing landmark detection methods

Abstract

In biomechanical modeling, the representation of ligament attachments is crucial for a realistic simulation of the forces acting between the vertebrae. These forces are typically modeled as vectors connecting ligament landmarks on adjacent vertebrae, making precise identification of these landmarks a key requirement for constructing reliable spine models. Existing automated detection methods are either limited to specific spinal regions or lack sufficient accuracy. This work presents a novel approach for detecting spinal ligament landmarks, which first performs shape-based segmentation of 3D vertebrae and subsequently applies domain-specific rules to identify different types of attachment points. The proposed method outperforms existing approaches by achieving high accuracy and demonstrating strong generalization across all spinal regions. Validation on two independent spinal datasets from multiple patients yielded a mean absolute error (MAE) of 0.7 mm and a root mean square error (RMSE) of 1.1 mm.