AI Approach for MRI-only Full-Spine Vertebral Segmentation and 3D Reconstruction in Paediatric Scoliosis

TL;DR

This paper presents an AI framework that automates MRI-based 3D spine segmentation and reconstruction in pediatric scoliosis, reducing processing time and eliminating radiation exposure.

Contribution

The study introduces a novel AI method combining GAN-generated MRI-like images and a U-Net model for fully automated spine segmentation and 3D reconstruction from MRI.

Findings

Achieved 88% Dice score in segmentation accuracy.

Reduced processing time from 1 hour to under one minute.

Enabled radiation-free 3D spine assessment in pediatric patients.

Abstract

MRI is preferred over CT in paediatric imaging because it avoids ionising radiation, but its use in spine deformity assessment is largely limited by the lack of automated, high-resolution 3D bony reconstruction, which continues to rely on CT. MRI-based 3D reconstruction remains impractical due to manual workflows and the scarcity of labelled full-spine datasets. This study introduces an AI framework that enables fully automated thoracolumbar spine (T1-L5) segmentation and 3D reconstruction from MRI alone. Historical low-dose CT scans from adolescent idiopathic scoliosis (AIS) patients were converted into MRI-like images using a GAN and combined with existing labelled thoracic MRI data to train a U-Net-based model. The resulting algorithm accurately generated continuous thoracolumbar 3D reconstructions, improved segmentation accuracy (88% Dice score), and reduced processing time from approximately 1 hour to under one minute, while preserving AIS-specific deformity features. This approach enables radiation-free 3D deformity assessment from MRI, supporting clinical evaluation, surgical planning, and navigation in paediatric spine care.