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arxiv:2608.06914

RibAssist 3D: Biplanar Rib-Fracture Detection, Addressing, and Selective 3D Localization from CT-Derived Projections

Published on Aug 10
· Submitted by
Kabila Haile Soboka
on Aug 14
Authors:

Abstract

Cross-view pairing of rib fractures in orthogonal CT projections enables accurate 3D localization, but correspondence confidence remains the primary bottleneck.

Rib fractures are common and time-consuming to localize on computed tomography (CT). We ask whether fractures detected independently in two orthogonal CT-derived projections (anteroposterior and lateral) can be paired across views and triangulated into reliable 3D points at a controlled rate of false outputs, and we answer it with a staged diagnostic study. The projection geometry is exact, and given correct correspondence, localization is accurate (median 4.0 mm, 88% within 10 mm, 93.6% rib-exact). On a sealed 55-case cohort, a large share of fractures is in principle recoverable (61.1% dual-view availability, and a correct pair present in the candidate graph for 58.4% of fractures), yet the binding limitation is neither geometry nor localization but confidence-limited cross-view correspondence. A controlled detector-by-correspondence factorial attributes the operational gain to lateral-detector quality rather than the tested matching methods; retraining the lateral detector produces the first nonzero controlled-budget reconstructions. Under a deliberately conservative commitment policy, a pre-specified sealed pass promotes 15 of 601 fractures to correct 3D localizations at 0.436 false points per case (2.50% end-to-end commitment yield), and committed points are accurate (median 1.49 mm, 93% rib-exact). The low yield is a consequence of confidence-gated abstention, not of geometry or detection: the study establishes a reproducible framework for selective 3D localization and identifies cross-view correspondence as the dominant operational bottleneck.

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Paper submitter

We introduce RibAssist 3D, an open-source research prototype for biplanar rib-fracture detection, anatomical addressing, and selective 3D localization from CT-derived AP and lateral projections.

Rather than forcing a 3D prediction for every detection, the system explicitly models uncertainty and abstains when cross-view evidence is insufficient. The work explores how anatomically constrained multi-view reasoning can support interpretable and reliable 3D fracture localization while exposing the current limitations and opportunities for further research.

Code and demo are publicly available.

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