  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

Computer Vision and Pattern Recognition;Machine Learning