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               <dc:title>Landmark anything: multi-view consensus convolutional networks applied to the 3D landmarking of Anatomical Structures</dc:title>
               <dc:creator>Heredia Lidón, Álvaro</dc:creator>
               <dc:creator>García Mascarel, Christian</dc:creator>
               <dc:creator>Echeverry, Luis Miguel</dc:creator>
               <dc:creator>Herrera Escartín, Daniel</dc:creator>
               <dc:creator>Fortea Ormaechea, Juan</dc:creator>
               <dc:creator>Pomarol-Clotet, Edith</dc:creator>
               <dc:creator>Fatjó-Vilas Mestre, Mar</dc:creator>
               <dc:creator>Martínez Abadías, Neus, 1978-</dc:creator>
               <dc:creator>Sevillano, Xavier</dc:creator>
               <dc:subject>Intel·ligència artificial</dc:subject>
               <dc:subject>Marcadors bioquímics</dc:subject>
               <dc:subject>Artificial intelligence</dc:subject>
               <dc:subject>Biochemical markers</dc:subject>
               <dc:description>As shape alterations in three-dimensional biological structures are as-&#xd;
sociated 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,&#xd;
while analyzing the impact of different network hyperparameters on the results.</dc:description>
               <dc:date>2025-03-04T15:23:11Z</dc:date>
               <dc:date>2025-03-04T15:23:11Z</dc:date>
               <dc:date>2024</dc:date>
               <dc:type>info:eu-repo/semantics/bookPart</dc:type>
               <dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
               <dc:relation>Reproducció del document publicat a: https://doi.org/10.3233/FAIA240438</dc:relation>
               <dc:relation>Capítol del llibre: Alsinet, Teresa,  Vilasís, Xavier , García, Daniel, Álvarez, Elena (eds.), Proceedings of the 26th International Conference of the Catalan Association for Artificial Intelligence, IOS Press, 2024, [ISBN 9781643685434], pp. 209-212</dc:relation>
               <dc:relation>https://doi.org/10.3233/FAIA240438</dc:relation>
               <dc:rights>cc by-nc (c) Heredia Lidón, Álvaro et al, 2024</dc:rights>
               <dc:rights>http://creativecommons.org/licenses/by-nc/3.0/es/</dc:rights>
               <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
               <dc:publisher>IOS Press</dc:publisher>
               <dc:source>Llibres / Capítols de llibre (Biologia Evolutiva, Ecologia i Ciències Ambientals)</dc:source>
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