Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/90519
Título: | Realistic 3D infant head surfaces augmentation to improve AI-based diagnosis of cranial deformities |
Autor(es): | Torres, Helena R. Oliveira, Bruno Morais, Pedro André Gonçalves Fritze, Anne Rüdiger, Mario Fonseca, Jaime C. Vilaça, João L. |
Palavras-chave: | 3D data augmentation Deep learning Head deformities Morphable models Motion transformation |
Data: | 2022 |
Editora: | Elsevier 1 |
Revista: | Journal of Biomedical Informatics |
Citação: | Torres, H. R., Oliveira, B., Morais, P., Fritze, A., Rüdiger, M., Fonseca, J. C., & Vilaça, J. L. (2022, August). Realistic 3D infant head surfaces augmentation to improve AI-based diagnosis of cranial deformities. Journal of Biomedical Informatics. Elsevier BV. http://doi.org/10.1016/j.jbi.2022.104121 |
Resumo(s): | Evaluation of the head shape of newborns is needed to detect cranial deformities, disturbances in head growth, and consequently, to predict short- and long-term neurodevelopment. Currently, there is a lack of automatic tools to provide a detailed evaluation of the head shape. Artificial intelligence (AI) methods, namely deep learning (DL), can be explored to develop fast and automatic approaches for shape evaluation. However, due to the clinical variability of patients’ head anatomy, generalization of AI networks to the clinical needs is paramount and extremely challenging. In this work, a new framework is proposed to augment the 3D data used for training DL networks for shape evaluation. The proposed augmentation strategy deforms head surfaces towards different deformities. For that, a point-based 3D morphable model (p3DMM) is developed to generate a statistical model representative of head shapes of different cranial deformities. Afterward, a constrained transformation approach (3DHT) is applied to warp a head surface towards a target deformity by estimating a dense motion field from a sparse one resulted from the p3DMM. Qualitative evaluation showed that the proposed method generates realistic head shapes indistinguishable from the real ones. Moreover, quantitative experiments demonstrated that DL networks training with the proposed augmented surfaces improves their performance in terms of head shape analysis. Overall, the introduced augmentation allows to effectively transform a given head surface towards different deformity shapes, potentiating the development of DL approaches for head shape analysis. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/90519 |
DOI: | 10.1016/j.jbi.2022.104121 |
ISSN: | 1532-0464 |
e-ISSN: | 1532-0480 |
Versão da editora: | https://www.sciencedirect.com/science/article/pii/S153204642200137X |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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realistic 3D infant.pdf | 6,55 MB | Adobe PDF | Ver/Abrir |