3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT

TL;DR

This paper introduces an automated 3D CNN-based system for detecting vertebral compression fractures in CT scans, achieving high accuracy and aiding osteoporosis management.

Contribution

It presents a novel end-to-end 3D sequence-to-sequence architecture combined with ensemble methods for improved fracture detection.

Findings

Achieved 0.955 AUC in patient-level fracture identification

Validated on a large dataset demonstrating state-of-the-art performance

Integrated a compact 3D spine representation for effective analysis

Abstract

An osteoporosis-related fracture occurs every three seconds worldwide, affecting one in three women and one in five men aged over 50. The early detection of at-risk patients facilitates effective and well-evidenced preventative interventions, reducing the incidence of major osteoporotic fractures. In this study, we present an automatic system for identification of vertebral compression fractures on Computed Tomography images, which are often an undiagnosed precursor to major osteoporosis-related fractures. The system integrates a compact 3D representation of the spine, utilizing a Convolutional Neural Network (CNN) for spinal cord detection and a novel end-to-end sequence to sequence 3D architecture. We evaluate several model variants that exploit different representation and classification approaches and present a framework combining an ensemble of models that achieves state of the art results, validated on a large data set, with a patient-level fracture identification of 0.955 Area Under the Curve (AUC). The system proposed has the potential to support osteoporosis clinical management, improve treatment pathways, and to change the course of one of the most burdensome diseases of our generation.