Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/90561
Título: | Fetal head circumference delineation using convolutional neural networks with registration-based ellipse fitting |
Autor(es): | Torres, Helena R. Oliveira, Bruno Morais, Pedro André Gonçalves Fritze, Anne Birdir, Cahit Rüdiger, Mario Fonseca, Jaime C. Vilaça, João L. |
Palavras-chave: | convolutional neural networks fetal head head circumference registration ultrasound |
Data: | Jan-2022 |
Editora: | Society of Photo-optical Instrumentation Engineers (SPIE) |
Revista: | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
Citação: | Torres, H. R., Oliveira, B., Morais, P. R., Fritze, A., Birdir, C., Rüdiger, M., ... & Vilaça, J. L. (2022, April). Fetal head circumference delineation using convolutional neural networks with registration-based ellipse fitting. In Medical Imaging 2022: Image Processing (Vol. 12032, pp. 927-933). SPIE. |
Resumo(s): | Examination of head shape during the fetal period is an important task to evaluate head growth and to diagnose fetal abnormalities. Traditional clinical practice frequently relies on the estimation of head circumference (HC) from 2D ultrasound (US) images by manually fitting an ellipse to the fetal skull. However, this process tends to be prone to observer variability, and therefore, automatic approaches for HC delineation can bring added value for clinical practice. In this paper, an automatic method to accurately delineate the fetal head in US images is proposed. The proposed method is divided into two stages: (i) head delineation through a regression convolutional neural network (CNN) that estimates a gaussian-like map of the head contour; and (ii) robust ellipse fitting using a registration-based approach that combines the random sample consensus (RANSAC) and iterative closest point (ICP) algorithms. The proposed method was applied to the HC18 Challenge dataset, which contains 999 training and 335 testing images. Experiments showed that the proposed strategy achieved a mean average difference of -0.11 ± 2.67 mm and a Dice coefficient of 97.95 ± 1.12% against manual annotation, outperforming other approaches in the literature. The obtained results showed the effectiveness of the proposed method for HC delineation, suggesting its potential to be used in clinical practice for head shape assessment. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/90561 |
ISBN: | 9781510649392 |
DOI: | 10.1117/12.2611150 |
ISSN: | 1605-7422 |
Versão da editora: | https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12032/120323L/Fetal-head-circumference-delineation-using-convolutional-neural-networks-with-registration/10.1117/12.2611150.short#_=_ |
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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Fetal head circumference delineation using convolutional neural networks with registration-based ellipse fitting.pdf | 787,61 kB | Adobe PDF | Ver/Abrir |