# Convolutional Neural Networks based automated segmentation and labelling of the lumbar spine X-ray

Source: [Convolutional Neural Networks based automated segmentation and labelling of the lumbar spine X-ray](<https://www.sota2.com/research/paper/68fa67511e209131cea156ba>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:11:14.110Z

Authors: Sandor Konya, Sai Natarajan T R, Hassan Allouch, Kais Abu Nahleh, Omneya Yakout Dogheim, Heinrich Boehm

Published: 2020-04-04T20:15:03.000Z

## Abstract

The aim of this study is to investigate the segmentation accuracies of different segmentation networks trained on 730 manually annotated lateral lumbar spine X-rays. Instance segmentation networks were compared to semantic segmentation networks. The study cohort comprised diseased spines and postoperative images with metallic implants. The average mean accuracy and mean intersection over union (IoU) was up to 3 percent better for the best performing instance segmentation model, the average pixel accuracy and weighted IoU were slightly better for the best performing semantic segmentation model. Moreover, the inferences of the instance segmentation models are easier to implement for further processing pipelines in clinical decision support.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.03364>)

[PDF](<https://arxiv.org/pdf/2004.03364.pdf>)