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.
@article{arxiv.2505.05004,
title = {Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort},
author = {Hendrik Möller and Hanna Schön and Alina Dima and Benjamin Keinert-Weth and Robert Graf and Matan Atad and Johannes Paetzold and Friederike Jungmann and Rickmer Braren and Florian Kofler and Bjoern Menze and Daniel Rueckert and Jan S. Kirschke},
journal= {arXiv preprint arXiv:2505.05004},
year = {2025}
}