Deep Learning Based Rib Centerline Extraction and Labeling

TL;DR

This paper presents a deep learning and algorithmic approach for fast, accurate extraction and labeling of rib centerlines from CT scans, aiding radiologists in efficient rib inspection.

Contribution

It introduces a combined deep learning detection and novel centerline extraction method for rib labeling in CT volumes, improving accuracy and robustness.

Findings

Achieved 0.787 mm centerline accuracy on 116 patient CT scans.

Successfully distinguished first and twelfth ribs for individual labeling.

Demonstrated robustness across diverse challenging CT datasets.

Abstract

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