Spinal ligaments detection on vertebrae meshes using registration and 3D edge detection

TL;DR

This paper presents a fast, accurate pipeline for detecting spinal ligament attachment points on vertebrae meshes, combining registration and edge detection to improve biomechanical modeling.

Contribution

It introduces a novel step-wise method that efficiently identifies 66 ligament landmarks with high accuracy and reduced processing time.

Findings

Average landmark detection error: 2.24 mm for anterior ligaments

Detection time per vertebra: approximately 3 seconds

Method outperforms existing approaches in speed and accuracy

Abstract

Spinal ligaments are crucial elements in the complex biomechanical simulation models as they transfer forces on the bony structure, guide and limit movements and stabilize the spine. The spinal ligaments encompass seven major groups being responsible for maintaining functional interrelationships among the other spinal components. Determination of the ligament origin and insertion points on the 3D vertebrae models is an essential step in building accurate and complex spine biomechanical models. In our paper, we propose a pipeline that is able to detect 66 spinal ligament attachment points by using a step-wise approach. Our method incorporates a fast vertebra registration that strategically extracts only 15 3D points to compute the transformation, and edge detection for a precise projection of the registered ligaments onto any given patient-specific vertebra model. Our method shows high accuracy, particularly in identifying landmarks on the anterior part of the vertebra with an average distance of 2.24 mm for anterior longitudinal ligament and 1.26 mm for posterior longitudinal ligament landmarks. The landmark detection requires approximately 3.0 seconds per vertebra, providing a substantial improvement over existing methods. Clinical relevance: using the proposed method, the required landmarks that represent origin and insertion points for forces in the biomechanical spine models can be localized automatically in an accurate and time-efficient manner.