Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387640507> ?p ?o ?g. }
Showing items 1 to 79 of
79
with 100 items per page.
- W4387640507 endingPage "103" @default.
- W4387640507 startingPage "94" @default.
- W4387640507 abstract "Accurate tooth segmentation on 3D dental models is an important task in computer-aided dentistry. In recent years, several deep learning-based methods have been proposed for automatic tooth segmentation. However, previous tooth segmentation methods often face challenges in accurately delineating boundaries, leading to a decline in overall segmentation performance. In this paper, we propose a boundary-constrained graph-based neural network that establishes the connectivity of mesh cells based on feature distances and utilizes several modules to encode local regions. To enhance segmentation performance in tooth-gingiva boundary regions, we integrate an auxiliary loss to segment the tooth and gingiva. Furthermore, to improve the performance in tooth-tooth boundary regions, we introduce a contrastive boundary-constrained loss that specifically enhances the distinctiveness of features within boundary mesh cells. Following the network prediction, we apply a post-processing step based on the graph cut to refine the boundaries. Experimental results demonstrate that our method achieves state-of-the-art performance in 3D tooth segmentation." @default.
- W4387640507 created "2023-10-15" @default.
- W4387640507 creator A5031035130 @default.
- W4387640507 creator A5060817282 @default.
- W4387640507 date "2023-10-15" @default.
- W4387640507 modified "2023-10-15" @default.
- W4387640507 title "Boundary-Constrained Graph Network for Tooth Segmentation on 3D Dental Surfaces" @default.
- W4387640507 cites W2211722331 @default.
- W4387640507 cites W2556802233 @default.
- W4387640507 cites W2804967795 @default.
- W4387640507 cites W2951864735 @default.
- W4387640507 cites W2963231572 @default.
- W4387640507 cites W2979750740 @default.
- W4387640507 cites W2990045899 @default.
- W4387640507 cites W3004849003 @default.
- W4387640507 cites W3107406728 @default.
- W4387640507 cites W3117388369 @default.
- W4387640507 cites W3138711038 @default.
- W4387640507 cites W3153465022 @default.
- W4387640507 cites W3184403467 @default.
- W4387640507 cites W3201585106 @default.
- W4387640507 cites W3207328575 @default.
- W4387640507 cites W4214755140 @default.
- W4387640507 cites W4224979443 @default.
- W4387640507 cites W4225353239 @default.
- W4387640507 cites W4226106508 @default.
- W4387640507 doi "https://doi.org/10.1007/978-3-031-45676-3_10" @default.
- W4387640507 hasPublicationYear "2023" @default.
- W4387640507 type Work @default.
- W4387640507 citedByCount "0" @default.
- W4387640507 crossrefType "book-chapter" @default.
- W4387640507 hasAuthorship W4387640507A5031035130 @default.
- W4387640507 hasAuthorship W4387640507A5060817282 @default.
- W4387640507 hasConcept C124504099 @default.
- W4387640507 hasConcept C132525143 @default.
- W4387640507 hasConcept C134306372 @default.
- W4387640507 hasConcept C138885662 @default.
- W4387640507 hasConcept C153180895 @default.
- W4387640507 hasConcept C154945302 @default.
- W4387640507 hasConcept C2776401178 @default.
- W4387640507 hasConcept C31972630 @default.
- W4387640507 hasConcept C33923547 @default.
- W4387640507 hasConcept C41008148 @default.
- W4387640507 hasConcept C41895202 @default.
- W4387640507 hasConcept C62354387 @default.
- W4387640507 hasConcept C80444323 @default.
- W4387640507 hasConcept C89600930 @default.
- W4387640507 hasConceptScore W4387640507C124504099 @default.
- W4387640507 hasConceptScore W4387640507C132525143 @default.
- W4387640507 hasConceptScore W4387640507C134306372 @default.
- W4387640507 hasConceptScore W4387640507C138885662 @default.
- W4387640507 hasConceptScore W4387640507C153180895 @default.
- W4387640507 hasConceptScore W4387640507C154945302 @default.
- W4387640507 hasConceptScore W4387640507C2776401178 @default.
- W4387640507 hasConceptScore W4387640507C31972630 @default.
- W4387640507 hasConceptScore W4387640507C33923547 @default.
- W4387640507 hasConceptScore W4387640507C41008148 @default.
- W4387640507 hasConceptScore W4387640507C41895202 @default.
- W4387640507 hasConceptScore W4387640507C62354387 @default.
- W4387640507 hasConceptScore W4387640507C80444323 @default.
- W4387640507 hasConceptScore W4387640507C89600930 @default.
- W4387640507 hasLocation W43876405071 @default.
- W4387640507 hasOpenAccess W4387640507 @default.
- W4387640507 hasPrimaryLocation W43876405071 @default.
- W4387640507 hasRelatedWork W1522196789 @default.
- W4387640507 hasRelatedWork W1992327129 @default.
- W4387640507 hasRelatedWork W2381986121 @default.
- W4387640507 hasRelatedWork W2501551404 @default.
- W4387640507 hasRelatedWork W2977677679 @default.
- W4387640507 hasRelatedWork W4298131179 @default.
- W4387640507 hasRelatedWork W4324315429 @default.
- W4387640507 hasRelatedWork W4366829857 @default.
- W4387640507 hasRelatedWork W4379231730 @default.
- W4387640507 hasRelatedWork W4385583601 @default.
- W4387640507 isParatext "false" @default.
- W4387640507 isRetracted "false" @default.
- W4387640507 workType "book-chapter" @default.