Vertebral Deformity Measurements at MRI, CT, and Radiography Using Deep Learning.

Purpose: To construct and evaluate the efficacy of a deep learning system to rapidly and automatically locate six vertebral landmarks, which are used to measure vertebral body heights, and to output spine angle measurements (lumbar lordosis angles [LLAs]) across multiple modalities.

Materials And Methods: In this retrospective study, MR ( = 1123), CT ( = 137), and radiographic ( = 484) images were used from a wide variety of patient populations, ages, disease stages, bone densities, and interventions ( = 1744 total patients, 64 years ± 8, 76.8% women; images acquired 2005-2020).

A deep learning system for automated, multi-modality 2D segmentation of vertebral bodies and intervertebral discs.

Purpose: Fractures in vertebral bodies are among the most common complications of osteoporosis and other bone diseases. However, studies that aim to predict future fractures and assess general spine health must manually delineate vertebral bodies and intervertebral discs in imaging studies for further radiomic analysis. This study aims to develop a deep learning system that can automatically and rapidly segment (delineate) vertebrae and discs in MR, CT, and X-ray imaging studies.