Anthropometric Landmark Detection in 3D Head Surfaces Using a Deep Learning Approach
| Autor: | Mario Rüdiger, Anne Fritze, Helena R. Torres, Pedro Morais, Fernando Veloso, João L. Vilaça, Jaime C. Fonseca, Bruno Oliveira |
|---|---|
| Přispěvatelé: | Universidade do Minho |
| Rok vydání: | 2021 |
| Předmět: |
convolutional networks
Cranial deformities landmark detection Computer science Head (linguistics) Cranial 02 engineering and technology Solid modeling 030218 nuclear medicine & medical imaging Task (project management) 03 medical and health sciences Imaging Three-Dimensional 0302 clinical medicine Health Information Management Robustness (computer science) Head growth Head surface 0202 electrical engineering electronic engineering information engineering Humans Electrical and Electronic Engineering Representation (mathematics) head growth Landmark detection Science & Technology Landmark Anthropometry business.industry Deep learning deep learning Shape Two dimensional displays Ciências Naturais::Ciências da Computação e da Informação Pattern recognition Magnetic heads Computer Science Applications Three-dimensional displays 020201 artificial intelligence & image processing Ciências da Computação e da Informação [Ciências Naturais] Artificial intelligence business Head Convolutional networks Biotechnology |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| ISSN: | 2168-2208 2168-2194 |
| DOI: | 10.1109/jbhi.2020.3035888 |
| Popis: | Landmark labeling in 3D head surfaces is an important and routine task in clinical practice to evaluate head shape, namely to analyze cranial deformities or growth evolution. However, manual labeling is still applied, being a tedious and time-consuming task, highly prone to intra-/inter-observer variability, and can mislead the diagnose. Thus, automatic methods for anthropometric landmark detection in 3D models have a high interest in clinical practice. In this paper, a novel framework is proposed to accurately detect landmarks in 3D infant’s head surfaces. The proposed method is divided into two stages: (i) 2D representation of the 3D head surface; and (ii) landmark detection through a deep learning strategy. Moreover, a 3D data augmentation method to create shape models based on the expected head variability is proposed. The proposed framework was evaluated in synthetic and real datasets, achieving accurate detection results. Furthermore, the data augmentation strategy proved its added value, increasing the method’s performance. Overall, the obtained results demonstrated the robustness of the proposed method and its potential to be used in clinical practice for head shape analysis. This work was funded by projects “NORTE-01-0145-FEDER-024300” and “NORTE-01-0145-FEDER-000045”, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). It was also funded by national funds, through the FCT – Fundação para a Ciência e Tecnologia and FCT/MCTES in the scope of the project UIDB/05549/2020 and UIDP/05549/2020. The authors also acknowledge support from FCT and the European Social Found, through Programa Operacional Capital Humano (POCH), in the scope of the PhD grant SFRH/BD/136670/2018, SFRH/BD/136721/2018, and SFRH/BD/131545/2017. |
| Databáze: | OpenAIRE |
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