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.</p>\n<p>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.</p>\n<p>Code and demo are publicly available.</p>\n","updatedAt":"2026-08-14T17:37:24.682Z","author":{"_id":"655474d3fc2c1019ab434f05","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/9JbRh8pSPrAubz7HXYVBn.png","fullname":"Kabila Haile Soboka","name":"kabilasoboka","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.870766282081604},"editors":["kabilasoboka"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/9JbRh8pSPrAubz7HXYVBn.png"],"reactions":[],"isReport":false}},{"id":"6a7fc1b8dad463e6faa3f277","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":378,"isUserFollowing":false},"createdAt":"2026-08-15T01:32:40.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Learning Dense 2D-3D Correspondence for X-ray-to-CT Registration of Knee Bones](https://huggingface.co/papers/2607.22803) (2026)\n* [APCReg: Anatomical-Prior-Guided Coarse-to-Fine CBCT--IOS Registration via Multi-View Projection and Reliability-Controlled Residual Correction](https://huggingface.co/papers/2608.09993) (2026)\n* [BODIESReg: An Open-Source Pipeline for Registering 3D Body Scans Using Pose-Aligned Initialization](https://huggingface.co/papers/2607.15463) (2026)\n* [ALTER: Modeling Longitudinal Changes via Regional Differencing for 3D CT Report Generation](https://huggingface.co/papers/2608.05615) (2026)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"<p>This is an automated message from the <a href=\"https://huggingface.co/librarian-bots\">Librarian Bot</a>. I found the following papers similar to this paper. </p>\n<p>The following papers were recommended by the Semantic Scholar API </p>\n<ul>\n<li><a href=\"https://huggingface.co/papers/2607.22803\">Learning Dense 2D-3D Correspondence for X-ray-to-CT Registration of Knee Bones</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2608.09993\">APCReg: Anatomical-Prior-Guided Coarse-to-Fine CBCT--IOS Registration via Multi-View Projection and Reliability-Controlled Residual Correction</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2607.15463\">BODIESReg: An Open-Source Pipeline for Registering 3D Body Scans Using Pose-Aligned Initialization</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2608.05615\">ALTER: Modeling Longitudinal Changes via Regional Differencing for 3D CT Report Generation</a> (2026)</li>\n</ul>\n<p> Please give a thumbs up to this comment if you found it helpful!</p>\n<p> If you want recommendations for any Paper on Hugging Face checkout <a href=\"https://huggingface.co/spaces/librarian-bots/recommend_similar_papers\">this</a> Space</p>\n<p> You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: <code>@librarian-bot recommend</code></p>\n","updatedAt":"2026-08-15T01:32:40.110Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":378,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7471933364868164},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2608.06914","authors":[{"_id":"6a7923988e9301703eaa5dff","name":"Kabila Haile Soboka","hidden":false}],"mediaUrls":["https://cdn-uploads.huggingface.co/production/uploads/655474d3fc2c1019ab434f05/VsUy7GzjaX2H4UAQ6bcR1.png"],"publishedAt":"2026-08-10T00:00:00.000Z","submittedOnDailyAt":"2026-08-14T00:00:00.000Z","title":"RibAssist 3D: Biplanar Rib-Fracture Detection, Addressing, and Selective 3D Localization from CT-Derived Projections","submittedOnDailyBy":{"_id":"655474d3fc2c1019ab434f05","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/9JbRh8pSPrAubz7HXYVBn.png","isPro":true,"fullname":"Kabila Haile Soboka","user":"kabilasoboka","type":"user","name":"kabilasoboka"},"summary":"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.","upvotes":0,"discussionId":"6a7923988e9301703eaa5e00","githubRepo":"https://github.com/kabJhai/RibAssist-3D","githubRepoAddedBy":"user","ai_summary":"Cross-view pairing of rib fractures in orthogonal CT projections enables accurate 3D localization, but correspondence confidence remains the primary bottleneck.","ai_keywords":["cross-view correspondence","orthogonal projections","triangulation","3D localization","confidence-gated abstention","factorial analysis","rib fracture detection"],"ai_summary_model":"thinkingmachines/Inkling-Small","githubStars":2},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[],"acceptLanguages":["en"],"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2608/2608.06914.md","query":{}}">
RibAssist 3D: Biplanar Rib-Fracture Detection, Addressing, and Selective 3D Localization from CT-Derived Projections
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.
Community
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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