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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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11517-026-03576-2","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T03:51:11Z","timestamp":1779421871000},"page":"2405-2419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual sagittal-guided coarse-to-fine rib segmentation and numbering in chest CT"],"prefix":"10.1007","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1407-8420","authenticated-orcid":false,"given":"Seonghyeon","family":"Ko","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duc-Tai","family":"Le","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junghyun","family":"Bum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyunseung","family":"Choo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,22]]},"reference":[{"key":"3576_CR1","doi-asserted-by":"crossref","unstructured":"Zhang T,\u00a0Pang H,\u00a0Wu Y,\u00a0Xu J,\u00a0Liang Z,\u00a0Xia S,\u00a0Jin C,\u00a0Chen R,\u00a0Qi S (2025) Inspirationonly: synthesizing expiratory ct from inspiratory ct to estimate parametric response map. 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