Análise da Quantidade de Sementes Necessárias para a Segmentação Semi-Automática em Imagens de TC de Pulmão Utilizando Crescimento de Região
Resumo
Dentre as categorias de câncer, o câncer de pulmão é o terceiro mais incidente e detém a maior taxa de mortalidade mundial. Doenças pulmonares, como câncer e ILDs (Interstitial Lung Disease), possuem uma maior possibilidade de cura quando diagnosticadas precocemente. Visando contribuir para a realização desses diagnósticos, essa pesquisa propõe um método semi-automático de segmentação de pulmão baseado na técnica de crescimento de região e apresenta uma análise do impacto da localização e quantidade das sementes utilizadas. Diversos experimentos foram realizados em duas bases, HUG-ILD e Vessel 12, obtendo coeficientes Dice de 96,94% ± 1,41% e 96,83% ± 0,92%, respectivamente.
Referências
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Thoracic (2020) “Interstitial Lung Disease” https://www.thoracic.org/patients/patient-resources/breathing-in-america/resources/chap ter-10-interstitial-lung-disease.pdf. Acessado em: 05/03/2020.
Vannier, M. W., El-Baz, A., Beache, G. M., Gimel'farb, G., Suzuki, K., Okada, K., Elnakib, A., Soliman, A., Abdollahi, B. (2013) “Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies”. In: International Journal of Biomedical Imaging, vol. 2013.
Soliman, A., Kthalifa, F., Elnakib, A., El-Ghar, M. A., Dunlap, N., Wang, B., Gimelfarb, G., Keynton, R. and El-Baz, A. (2017) “Accurate lungs segmentation on CT chest images by adaptive appearance-guided shape modeling”, In: IEEE transactions on medical imaging, vol. 36, no. 1, p. 263–276.
Harrison, A. P., Xu, Z., George, K., Lu, L., Summers, R. M. and Mollura, D. J. (2017) “Progressive and multipath holistically nested neural networks for pathological lung segmentation from CT images”, In: Medical Image Computing and Computer-Assisted Intervention MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III. Springer International Publishing, 2017, p. 621–629.
Alves, J. H., Neto, P. M. M. and Oliveira, L. F. (2018) “Extracting Lungs from CT Images Using Fully Convolutional Networks”, In: 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, p. 1-8.
Depeursinge, A., Vargas, A., Platon, A., Geissbuhler, A., Poletti, P. and Müller, H.
(2012) “Building a Reference Multimedia Database for Interstitial Lung Diseases”, In: Computerized Medical Imaging and Graphics, vol. 36, no. 3, p. 227-238.
Rudyanto, R. D., Kerkstra, S., van Rikxoort, E. M., Fetita, C., Brillet, P.-Y., Lefevre, C., Xue, W., Zhu, X., Liang, J., Öksüz et al. (2014). “Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study”, In: Medical Image Analysis, vol. 18, no. 7, p. 1217-1232.
Zou, K. H., Warfield, S. K., Bharatha, A., Tempany, C. M., Kaus, M. R., Haker, S. J., Wells, W. M., Jolesz, F. A. and Kikinis, R. (2004) “Statistical validation of image segmentation quality based on a spatial overlap index: Scientific reports”, In: Academic radiology, vol. 11, no. 2, p. 178–189.
Publicado
15/09/2020
Como Citar
TRAIN, Glenda; ALVES, Jeovane; OLIVEIRA, Lucas Ferrari.
Análise da Quantidade de Sementes Necessárias para a Segmentação Semi-Automática em Imagens de TC de Pulmão Utilizando Crescimento de Região. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 96-107.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2020.11505.
