# Deep Learning Based Rib Centerline Extraction and Labeling

arXiv: https://arxiv.org/abs/1809.07082 · HTML: https://mlsanity.com/paper/1809.07082/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2018.

## Built on by (most cited first, 10 of 10)

- [RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans](https://mlsanity.com/paper/2109.09521/) (2021)
- [Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge](https://mlsanity.com/paper/2402.09372/) (2024)
- [RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction](https://mlsanity.com/paper/2210.09309/) (2022)
- [PointScatter: Point Set Representation for Tubular Structure Extraction](https://mlsanity.com/paper/2209.05774/) (2022)
- [Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding](https://mlsanity.com/paper/2212.02014/) (2022)
- [AnyCXR: Human Anatomy Segmentation of Chest X-ray at Any Acquisition Position using Multi-stage Domain Randomized Synthetic Data with Imperfect Annotations and Conditional Joint Annotation Regularization Learning](https://mlsanity.com/paper/2512.17263/) (2025)
- [Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort](https://mlsanity.com/paper/2505.05004/) (2025)
- [Hierarchical Loss And Geometric Mask Refinement For Multilabel Ribs Segmentation](https://mlsanity.com/paper/2405.15500/) (2024)
- [FleXray: Universal Clinical X-ray Segmentation](https://mlsanity.com/paper/2609.26756/) (2026)
- [Hybrid Context Forests and Bilateral Viterbi Matching for 3D Rib Centerline Tracing in CT](https://mlsanity.com/paper/2610.16047/) (2026)

## Abstract (arXiv)

Automated extraction and labeling of rib centerlines is a typically needed prerequisite for more advanced assisted reading tools that help the radiologist to efficiently inspect all 24 ribs in a CT volume. In this paper, we combine a deep learning-based rib detection with a dedicated centerline extraction algorithm applied to the detection result for the purpose of fast, robust and accurate rib centerline extraction and labeling from CT volumes. More specifically, we first apply a fully convolutional neural network (FCNN) to generate a probability map for detecting the first rib pair, the twelfth rib pair, and the collection of all intermediate ribs. In a second stage, a newly designed centerline extraction algorithm is applied to this multi-label probability map. Finally, the distinct detection of first and twelfth rib separately, allows to derive individual rib labels by simple sorting and counting the detected centerlines. We applied our method to CT volumes from 116 patients which included a variety of different challenges and achieved a centerline accuracy of 0.787 mm with respect to manual centerline annotations. This article is a preprint version of: Lenga M., Klinder T., B"urger C., von Berg J., Franz A., Lorenz C. (2019) Deep Learning Based Rib Centerline Extraction and Labeling. In: Vrtovec T., Yao J., Zheng G., Pozo J. (eds) Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2018. Lecture Notes in Computer Science, vol 11404. Springer, Cham