Landmark anything: Multi-view consensus convolutional networks applied to the 3D landmarking of anatomical structures
As shape alterations in three-dimensional biological structures are associated to numerous pathological processes, quantitative shape analysis for obtaining phenotypic biomarkers of diagnostic potential has become a prominent research area. In this context, the automatic detection of landmarks on 3D...
| Authors: | , , , , , , , , |
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| Format: | article |
| Publication Date: | 2024 |
| Country: | España |
| Institution: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repository: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:20.500.14342/5501 |
| Online Access: | http://hdl.handle.net/20.500.14342/5501 https://doi.org/:10.3233/FAIA240438 |
| Access Level: | Open access |
| Keyword: | Automatic 3D landmarking Multi-view convolutional networks Face Upper respiratory airways Hippocampus Biomakers 004 61 62 |
| Summary: | As shape alterations in three-dimensional biological structures are associated to numerous pathological processes, quantitative shape analysis for obtaining phenotypic biomarkers of diagnostic potential has become a prominent research area. In this context, the automatic detection of landmarks on 3D anatomical structures is crucial for developing high-throughput phenotyping tools. This study evaluates the performance of multi-view consensus convolutional networks – originally developed for facial landmarking– in automatically detecting landmarks on three different 3D anatomical structures: the face, the upper respiratory airways and the brain hippocampi. Leveraging magnetic resonance imaging datasets, we trained multiple models and assessed their accuracy against manual annotations, while analyzing the impact of different network hyperparameters on the results. |
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