Recuperação de Nódulos Pulmonares por Conteúdo: uma abordagem Radiomics em Pesquisa Reprodutível
Resumo
O diagnóstico e o tratamento precoce do câncer de pulmão são formas efetivas de garantir a vida dos pacientes. Contudo, a detecção e classificação dos nódulos pulmonares são tarefas desafiadoras aos especialistas, pois os nódulos são pequenos e apresentam baixo contraste. O objetivo deste trabalho foi avaliar a precisão dos atributos de Forma 3D e Intensidade 3D associados a atributos de Textura 3D e Nitidez de Borda disponíveis no contexto de pesquisa reprodutível. Os atributos formaram um vertor Radiomics de 66 dimensões e foram aplicados à recuperação de imagens por conteúdo. O resultado da precisão do algoritmo foi de 0,841 e 0,803 na recuperação de nódulos benignos e malignos, respectivamente.
Referências
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Ferreira Junior, J. R., Oliveira, M. C., and de Azevedo-Marques, P. M. (2016a). Cloud-based nosql open database of pulmonary nodules for computer-aided lung cancer diagnosis and reproducible research. Journal of Digital Imaging, pages 1–14.
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Kumar, D., Shafiee, M. J., Chung, A. G., Khalvati, F., Haider, M. A., and Wong, A. (2015). Discovery radiomics for computed tomography cancer detection. CoRR, abs/1509.00117.
Kuruvilla, J. and Gunavathi, K. (2014). Lung cancer classification using neural networks for {CT} images. Computer Methods and Programs in Biomedicine, 113(1):202 – 209.
Müller, H., Michoux, N., Bandon, D., and Geissbuhler, A. (2004). A review of content-based image retrieval systems in medical applications—clinical benefits and future directions. International Journal of Medical Informatics, 73(1):1 – 23.
Nowik, S. D. K. (2013). The use of surrounding lung parenchyma for the automated classification of pulmonary nodules. Ms (master of science) thesis, University of Iowa.
Oliveira, M. C. and Ferreira, J. R. (2013). A bag-of-tasks approach to speed up the lung nodules retrieval in the bigdata age. In e-Health Networking, Applications Services (Healthcom), 2013 IEEE 15th International Conference on, pages 632–636.
Wu, H. and He, L. (2015). Combining visual and textual features for medical image modality classification with lp-norm multiple kernel learning. Neurocomputing, 147:387 – 394. Advances in Self-Organizing Maps Subtitle of the special issue: Selected Papers from the Workshop on Self-Organizing Maps 2012 (WSOM 2012).
Akgül, C. B., Rubin, D. L., Napel, S., Beaulieu, C. F., Greenspan, H., and Acar, B. (2010). Content-based image retrieval in radiology: Current status and future directions. Journal of Digital Imaging, 24(2):208–222.
Armato, S. G., McLennan, G., Bidaut, L., et al. (2011). The lung image database consortium (lidc) and image database resource initiative (idri): A completed reference database of lung nodules on ct scans. Medical Physics, 38(2):915–931.
Brandman, Scott MD; Ko, J. P. M. (2011). Pulmonary nodule detection, characterization, and management with multidetector computed tomography. Journal of Thoracic Imaging, 26(2):90–105.
Ferreira de Lucena, D. J., Ferreira Junior, J. R., Machado, A. P., and Oliveira, M. C. (2016). Automatic weighing attribute to retrieve similar lung cancer nodules. BMC Medical Informatics and Decision Making, 16(2):79.
Ferreira Junior, J. R., Oliveira, M. C., and de Azevedo-Marques, P. M. (2016a). Cloud-based nosql open database of pulmonary nodules for computer-aided lung cancer diagnosis and reproducible research. Journal of Digital Imaging, pages 1–14.
Ferreira Junior, J. R., Oliveira, M. C., and de Azevedo-Marques, P. M. (2016b). Integrating 3d image descriptors of margin sharpness and texture on a gpu-optimized similar pulmonary nodule retrieval engine. The Journal of Supercomputing, pages 1–17.
Gillies, R. J., Kinahan, P. E., and Hricak, H. (2016). Radiomics: Images are more than pictures, they are data. Radiology, 278(2):563–577. PMID: 26579733.
INCA (2016). Instituto nacional de câncer. last accessed 22-09-2016.
Kumar, D., Shafiee, M. J., Chung, A. G., Khalvati, F., Haider, M. A., and Wong, A. (2015). Discovery radiomics for computed tomography cancer detection. CoRR, abs/1509.00117.
Kuruvilla, J. and Gunavathi, K. (2014). Lung cancer classification using neural networks for {CT} images. Computer Methods and Programs in Biomedicine, 113(1):202 – 209.
Müller, H., Michoux, N., Bandon, D., and Geissbuhler, A. (2004). A review of content-based image retrieval systems in medical applications—clinical benefits and future directions. International Journal of Medical Informatics, 73(1):1 – 23.
Nowik, S. D. K. (2013). The use of surrounding lung parenchyma for the automated classification of pulmonary nodules. Ms (master of science) thesis, University of Iowa.
Oliveira, M. C. and Ferreira, J. R. (2013). A bag-of-tasks approach to speed up the lung nodules retrieval in the bigdata age. In e-Health Networking, Applications Services (Healthcom), 2013 IEEE 15th International Conference on, pages 632–636.
Wu, H. and He, L. (2015). Combining visual and textual features for medical image modality classification with lp-norm multiple kernel learning. Neurocomputing, 147:387 – 394. Advances in Self-Organizing Maps Subtitle of the special issue: Selected Papers from the Workshop on Self-Organizing Maps 2012 (WSOM 2012).
Publicado
02/07/2017
Como Citar
OLIVEIRA, Marcelo Costa; DE LUCENA, David Jones Ferreira; FELIX, Ailton.
Recuperação de Nódulos Pulmonares por Conteúdo: uma abordagem Radiomics em Pesquisa Reprodutível. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 17. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 2048-2057.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2017.3723.
