Year: 2026
Type: article
Abstract: Rib identification in chest CT is time-consuming and labor-intensive, as clinicians repetitively examine hundreds of CT slices to pinpoint a certain rib on a random slice. Computer-aided rib numbering tools exist, however, their performance hinges on precise rib segmentation which is challenged by missing ribs or oversegmentation of adjacent bone structures. We propose Dual sagittal-guided Coarse-to-fine Rib Segmentation (DCRS), a lightweight and practical 3D rib segmentation approach that coarsely captures the entire rib structure within 2 sagittal slices and finely constructs 3D ribs. DCRS operates in three stages: (1) Region of Non-Interest (RONI) removal, a preprocessing stage which excludes the soft tissues, spine, sternum, and transverse processes to isolate ribs; (2) a lightweight U-Net applied to only two representative sagittal slices for obtaining coarse sagittal ribs; and (3) Six-Neighborhood Outward Flood-Filling (SNOFF), a fine 3D rib construction that expands sagittal rib predictions into full 3D ribs. This coarse-to-fine approach ensures a complete 12-pair rib mask while decreasing the complexity and computational cost compared to coarse slice, patch, or point-cloud-wise rib segmentation methods. The training and testing of the proposed DCRS utilize a public rib segmentation dataset. DCRS achieves a Dice score of 92.83% and IoU of 88.21%, exceeding prior state-of-the-art by 3.1% and 4.1%, respectively. DCRS enables fast and reliable rib numbering, increasing throughput by 44% compared to the prior state-of-the-art. Rib numbering takes only 0.5% additional time relative to standard CT, highlighting its clinical practicality.
Institution: Sungkyunkwan University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,990
Funder: Sungkyunkwan University
OpenAlex ID: W7162012759