Segmentação da Região Pulmonar em Radiografias Pediátricas de Tórax
Resumo
Nas radiografias de tórax (RXT), a identificação automática de regiões, estruturas ou objetos que a compõem pode auxiliar o profissional da área a realizar sua leitura e análise de forma mais assertiva, promovendo a melhoria e eficiência do diagnóstico. Este trabalho propõe um método para segmentar os campos pulmonares em RXT. Com base em operações de processamento digital de imagens e uso de regras, a proposta é avaliada em um banco de dados de imagens RXT pediátricas. Os resultados satisfatórios para diferentes classes de imagens analisadas indicam que o método proposto pode ser utilizado de forma prática em etapas de pré-processamento de fluxos mais complexos.
Referências
Candemir, S. and Antani, S. (2019). A review on lung boundary detection in chest x-rays. International journal of computer assisted radiology and surgery, 14(4):563–576.
Candemir, S., Antani, S., et al. (2015). Lung boundary detection in pediatric chest x-rays. In Proc. SPIE, Medical Imaging 2015: PACS and Imaging Informatics: Next Generation and Innovations, volume 9418, pages 94180Q–94180Q–6.
Candemir, S., Jaeger, S., et al. (2014). Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Transactions on Medical Imaging, 33(2):577–590.
Chen, J., Lu, Y., Yu, Q., et al. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306.
Clarke, L., Velthuizen, R., Camacho, M., et al. (1995). MRI segmentation: methods and applications. Magnetic resonance imaging, 13(3):343–368.
Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., et al. (2020). Inf-net: Automatic covid-19 lung infection segmentation from ct images. IEEE Transactions on Medical Imaging, 39(8):2626–2637.
Fonseca, A. U., Oliveira, L. L. G., and Soares, F. A. A. M. N. (2016). Detecção de artefatos estranhos em radiografias de torax. In XV CBIS - Congresso Brasileiro de Informatica em Saúde, pages 721–730, Goiânia, Brasil.
Gonzalez, R. C. and WOODS, R. E. (2010). Processamento digital de imagens. Pearson Prentice Hall, São Paulo, 3 edition.
Gordienko, Y., Gang, P., Hui, J., Zeng, W., et al. (2018). Deep learning with lung segmentation and bone shadow exclusion techniques for chest x-ray analysis of lung cancer. In International Conference on Computer Science, Engineering and Education Applications, pages 638–647. Springer.
Gu, Z., Cheng, J., Fu, H., et al. (2019). Ce-net: Context encoder network for 2d medical image segmentation. IEEE Transactions on Medical Imaging, 38(10):2281–2292.
Hesamian, M. H., Jia, W., He, X., and Kennedy, P. (2019). Deep learning techniques for medical image segmentation: achievements and challenges. Journal of digital imaging, 32(4):582–596.
Hogeweg, L., Sánchez, C. I., de Jong, P. A., et al. (2012). Clavicle segmentation in chest radiographs. Medical Image Analysis, 16(8):1490–1502.
Hogeweg, L., Sanchez, C. I., and Van Ginneken, B. (2013). Suppression of translucent elongated structures: Applications in chest radiography. IEEE Transactions on Medical Imaging, 32(11):2099–2113.
Iakovidis, D. K., Savelonas, M. A., and Papamichalis, G. (2009). Robust model-based detection of the lung field boundaries in portable chest radiographs supported by selective thresholding. Measurement Science and Technology, 20(10):104019.
Jaeger, S., Karargyris, A., Candemir, S., Siegelman, J., et al. (2013). Automatic screening for tuberculosis in chest radiographs: a survey. Quantitative imaging in medicine and surgery, 3(2):89–99.
Li, X., Luo, S., Hu, Q., Li, J., Wang, D., and Chiong, F. (2016). Automatic lung field segmentation in x-ray radiographs using statistical shape and appearance models. Journal of Medical Imaging and Health Informatics, 6(2):338–348.
Mahmood, F., Borders, D., Chen, R. J., Mckay, G. N., et al. (2020). Deep adversarial training for multi-organ nuclei segmentation in histopathology images. IEEE Transactions on Medical Imaging, 39(11):3257–3267.
Oh, Y., Park, S., and Ye, J. C. (2020). Deep learning covid-19 features on cxr using limited training data sets. IEEE transactions on medical imaging, 39(8):2688–2700.
Patil, S. and Udupi, D. V. (2012). Preprocessing to be considered for mr and ct images containing tumors. IOSR Journal Electrical and Electronics Engineering, 1(4):54–57.
Peng, T., Xu, T. C., Wang, Y., Zhou, H., et al. (2020). Hybrid automatic lung segmentation on chest ct scans. IEEE Access, 8:73293–73306.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer.
Schalekamp, S., Karssemeijer, N., Cats, A. M., De Hoop, B., et al. (2016). The effect of supplementary bone-suppressed chest radiographs on the assessment of a variety of common pulmonary abnormalities: Results of an observer study. Journal of thoracic imaging, 31(2):119–125.
Shi, F., Wang, J., Shi, J., Wu, Z., et al. (2021). Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for covid-19. IEEE Reviews in Biomedical Engineering, 14:4–15.
Taheri, M., Rastgarpour, M., and Koochari, A. (2021). A novel method for medical image segmentation based on convolutional neural networks with sgd optimization. Journal of Electrical and Computer Engineering Innovations (JECEI), 9(1):37–46.
Teixeira, L. O., Pereira, R. M., Bertolini, D., et al. (2020). Impact of lung segmentation on the diagnosis and explanation of covid-19 in chest x-ray images. arXiv preprint arXiv:2009.09780.
Tsevas, S. and Iakovidis, D. K. (2011). Measuring the relative extent of pulmonary infiltrates by hierarchical classification of patient-specific image features. Measurement Science and Technology, 22(11):114017.
Van Ginneken, B. (2001). Computer-aided diagnosis in chest radiography. PhD thesis, University Medical Center Utrecht.
Xue, Z., Candemir, S., Antani, S., Long, L. R., Jaeger, S., Demner-Fushman, D., and Thoma, G. R. (2015). Foreign object detection in chest x-rays. In Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on, pages 956–961. IEEE.
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep learning in medical image analysis and multimodal learning for clinical decision support, pages 3–11. Springer.
Candemir, S., Antani, S., et al. (2015). Lung boundary detection in pediatric chest x-rays. In Proc. SPIE, Medical Imaging 2015: PACS and Imaging Informatics: Next Generation and Innovations, volume 9418, pages 94180Q–94180Q–6.
Candemir, S., Jaeger, S., et al. (2014). Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Transactions on Medical Imaging, 33(2):577–590.
Chen, J., Lu, Y., Yu, Q., et al. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306.
Clarke, L., Velthuizen, R., Camacho, M., et al. (1995). MRI segmentation: methods and applications. Magnetic resonance imaging, 13(3):343–368.
Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., et al. (2020). Inf-net: Automatic covid-19 lung infection segmentation from ct images. IEEE Transactions on Medical Imaging, 39(8):2626–2637.
Fonseca, A. U., Oliveira, L. L. G., and Soares, F. A. A. M. N. (2016). Detecção de artefatos estranhos em radiografias de torax. In XV CBIS - Congresso Brasileiro de Informatica em Saúde, pages 721–730, Goiânia, Brasil.
Gonzalez, R. C. and WOODS, R. E. (2010). Processamento digital de imagens. Pearson Prentice Hall, São Paulo, 3 edition.
Gordienko, Y., Gang, P., Hui, J., Zeng, W., et al. (2018). Deep learning with lung segmentation and bone shadow exclusion techniques for chest x-ray analysis of lung cancer. In International Conference on Computer Science, Engineering and Education Applications, pages 638–647. Springer.
Gu, Z., Cheng, J., Fu, H., et al. (2019). Ce-net: Context encoder network for 2d medical image segmentation. IEEE Transactions on Medical Imaging, 38(10):2281–2292.
Hesamian, M. H., Jia, W., He, X., and Kennedy, P. (2019). Deep learning techniques for medical image segmentation: achievements and challenges. Journal of digital imaging, 32(4):582–596.
Hogeweg, L., Sánchez, C. I., de Jong, P. A., et al. (2012). Clavicle segmentation in chest radiographs. Medical Image Analysis, 16(8):1490–1502.
Hogeweg, L., Sanchez, C. I., and Van Ginneken, B. (2013). Suppression of translucent elongated structures: Applications in chest radiography. IEEE Transactions on Medical Imaging, 32(11):2099–2113.
Iakovidis, D. K., Savelonas, M. A., and Papamichalis, G. (2009). Robust model-based detection of the lung field boundaries in portable chest radiographs supported by selective thresholding. Measurement Science and Technology, 20(10):104019.
Jaeger, S., Karargyris, A., Candemir, S., Siegelman, J., et al. (2013). Automatic screening for tuberculosis in chest radiographs: a survey. Quantitative imaging in medicine and surgery, 3(2):89–99.
Li, X., Luo, S., Hu, Q., Li, J., Wang, D., and Chiong, F. (2016). Automatic lung field segmentation in x-ray radiographs using statistical shape and appearance models. Journal of Medical Imaging and Health Informatics, 6(2):338–348.
Mahmood, F., Borders, D., Chen, R. J., Mckay, G. N., et al. (2020). Deep adversarial training for multi-organ nuclei segmentation in histopathology images. IEEE Transactions on Medical Imaging, 39(11):3257–3267.
Oh, Y., Park, S., and Ye, J. C. (2020). Deep learning covid-19 features on cxr using limited training data sets. IEEE transactions on medical imaging, 39(8):2688–2700.
Patil, S. and Udupi, D. V. (2012). Preprocessing to be considered for mr and ct images containing tumors. IOSR Journal Electrical and Electronics Engineering, 1(4):54–57.
Peng, T., Xu, T. C., Wang, Y., Zhou, H., et al. (2020). Hybrid automatic lung segmentation on chest ct scans. IEEE Access, 8:73293–73306.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer.
Schalekamp, S., Karssemeijer, N., Cats, A. M., De Hoop, B., et al. (2016). The effect of supplementary bone-suppressed chest radiographs on the assessment of a variety of common pulmonary abnormalities: Results of an observer study. Journal of thoracic imaging, 31(2):119–125.
Shi, F., Wang, J., Shi, J., Wu, Z., et al. (2021). Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for covid-19. IEEE Reviews in Biomedical Engineering, 14:4–15.
Taheri, M., Rastgarpour, M., and Koochari, A. (2021). A novel method for medical image segmentation based on convolutional neural networks with sgd optimization. Journal of Electrical and Computer Engineering Innovations (JECEI), 9(1):37–46.
Teixeira, L. O., Pereira, R. M., Bertolini, D., et al. (2020). Impact of lung segmentation on the diagnosis and explanation of covid-19 in chest x-ray images. arXiv preprint arXiv:2009.09780.
Tsevas, S. and Iakovidis, D. K. (2011). Measuring the relative extent of pulmonary infiltrates by hierarchical classification of patient-specific image features. Measurement Science and Technology, 22(11):114017.
Van Ginneken, B. (2001). Computer-aided diagnosis in chest radiography. PhD thesis, University Medical Center Utrecht.
Xue, Z., Candemir, S., Antani, S., Long, L. R., Jaeger, S., Demner-Fushman, D., and Thoma, G. R. (2015). Foreign object detection in chest x-rays. In Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on, pages 956–961. IEEE.
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep learning in medical image analysis and multimodal learning for clinical decision support, pages 3–11. Springer.
Publicado
25/10/2022
Como Citar
DA FONSECA, Afonso Ueslei; FELIX, Juliana Paula; SOARES, Fabrizzio.
Segmentação da Região Pulmonar em Radiografias Pediátricas de Tórax. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 10. , 2022, Goiás.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 48-59.
DOI: https://doi.org/10.5753/erigo.2022.227693.
