TY - GEN
T1 - Automated Orientation Detection of 3D Head Reconstructions from sMRI Using Multiview Orthographic Projections
T2 - 11th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2023
AU - Heredia-Lidón, Álvaro
AU - González, Alejandro
AU - Guerrero-Mosquera, Carlos
AU - Gonzàlez-Colom, Rubèn
AU - Echeverry, Luis M.
AU - Hostalet, Noemí
AU - Salvador, Raymond
AU - Pomarol-Clotet, Edith
AU - Fortea, Juan
AU - Martínez-Abadías, Neus
AU - Fatjó-Vilas, Mar
AU - Sevillano, Xavier
N1 - Funding Information:
The research was supported by the Joan Oró grant (FI 2022) from the DRU of the Generalitat de Catalunya and the European Social Fund (2023 FI-2 00160). The authors would also like to thank the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) of the Generalitat de Catalunya (2021 SGR01396, 2021 SGR00706, 2021 SGR1475), the Spanish Ministry of Science, Innovation, and Universities under grant PID2020-113609RB-C21, and Fondation Jerome Lejeune under grant 2020b cycle-Project No.2001.
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Recent studies in neuropsychiatry have highlighted the correlation between facial and brain dysmorphologies. One way of simultaneously analysing the brain and the face of a subject is by reconstructing a whole-head 3D model from structural magnetic resonance imaging (sMRI). However, the use of different reconstruction protocols generates undesired orthogonal rotations of the 3D models. This is a likely situation in multicentric studies that hampers the combination of data from different centers. Although the original sMRI files contain the subject orientation, it is not always possible to access this data. To solve this issue, in this work we propose a novel method to estimate the orientation of 3D heads with rotations of 90 ∘ or multiples thereof around any of the three Cartesian axes as a required step for generating a normalised dataset in terms of orientation. Our proposal creates 2D images from orthogonal projections of the 3D object, transforming orientation estimation into an image classification problem. Experimental results show that our method, using three orthographic views of the 3D head to create the projection image and ResNet50 for classification, achieves an accuracy of 99.7%, which corresponds to 0.15 mean absolute error in rotation, outperforming state-of-the-art point cloud registration methods like DeepBBS and PRNet.
AB - Recent studies in neuropsychiatry have highlighted the correlation between facial and brain dysmorphologies. One way of simultaneously analysing the brain and the face of a subject is by reconstructing a whole-head 3D model from structural magnetic resonance imaging (sMRI). However, the use of different reconstruction protocols generates undesired orthogonal rotations of the 3D models. This is a likely situation in multicentric studies that hampers the combination of data from different centers. Although the original sMRI files contain the subject orientation, it is not always possible to access this data. To solve this issue, in this work we propose a novel method to estimate the orientation of 3D heads with rotations of 90 ∘ or multiples thereof around any of the three Cartesian axes as a required step for generating a normalised dataset in terms of orientation. Our proposal creates 2D images from orthogonal projections of the 3D object, transforming orientation estimation into an image classification problem. Experimental results show that our method, using three orthographic views of the 3D head to create the projection image and ResNet50 for classification, achieves an accuracy of 99.7%, which corresponds to 0.15 mean absolute error in rotation, outperforming state-of-the-art point cloud registration methods like DeepBBS and PRNet.
KW - 3D head orientation
KW - Image classification
KW - Multiview orthographic projections
KW - Point cloud registration
KW - Structural magnetic resonance imaging
UR - http://www.scopus.com/inward/record.url?scp=85164965624&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-36616-1_48
DO - 10.1007/978-3-031-36616-1_48
M3 - Conference contribution
AN - SCOPUS:85164965624
SN - 9783031366154
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 603
EP - 614
BT - Pattern Recognition and Image Analysis - 11th Iberian Conference, IbPRIA 2023, Proceedings
A2 - Pertusa, Antonio
A2 - Gallego, Antonio Javier
A2 - Sánchez, Joan Andreu
A2 - Domingues, Inês
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 27 June 2023 through 30 June 2023
ER -