Convolutional neural network-based automated segmentation and labeling of the lumbar spine X-ray.

ABSTRACT:

Purpose

This study investigated the segmentation metrics of different segmentation networks trained on 730 manually annotated lateral lumbar spine X-rays to test the generalization ability and robustness which are the basis of clinical decision support algorithms.

Methods

Instance segmentation networks were compared to semantic segmentation networks based on different metrics. The study cohort comprised diseased spines and postoperative images with metallic implants.

Results

However, the pixel accuracies and intersection over union are similarly high for the best performing instance and semantic segmentation models; the observed vertebral recognition rates of the instance segmentation models statistically significantly outperform the semantic models' recognition rates

SUBMITTER: Konya S 

PROVIDER: S-EPMC8214241 | biostudies-literature | 2021 Apr-Jun

REPOSITORIES: biostudies-literature