Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort

TL;DR

This study develops a high-accuracy deep learning method for automated detection and morphological analysis of thoracolumbar stump ribs in CT scans, providing quantitative insights and publicly available tools.

Contribution

It introduces a novel deep learning model for rib segmentation with significant accuracy improvements and a method for quantitative morphological analysis of stump ribs.

Findings

Deep learning model achieved Dice score of 0.997, outperforming previous models.

Ribs were successfully analyzed with a 98.2% success rate in length assessment.

Stump ribs exhibit distinct morphological features, aiding automatic differentiation.

Abstract

Thoracolumbar stump ribs are one of the essential indicators of thoracolumbar transitional vertebrae or enumeration anomalies. While some studies manually assess these anomalies and describe the ribs qualitatively, this study aims to automate thoracolumbar stump rib detection and analyze their morphology quantitatively. To this end, we train a high-resolution deep-learning model for rib segmentation and show significant improvements compared to existing models (Dice score 0.997 vs. 0.779, p-value < 0.01). In addition, we use an iterative algorithm and piece-wise linear interpolation to assess the length of the ribs, showing a success rate of 98.2%. When analyzing morphological features, we show that stump ribs articulate more posteriorly at the vertebrae (-19.2 +- 3.8 vs -13.8 +- 2.5, p-value < 0.01), are thinner (260.6 +- 103.4 vs. 563.6 +- 127.1, p-value < 0.01), and are oriented more downwards and sideways within the first centimeters in contrast to full-length ribs. We show that with partially visible ribs, these features can achieve an F1-score of 0.84 in differentiating stump ribs from regular ones. We publish the model weights and masks for public use.