Methods for segmentation of spinal cord and esophagus in radiotherapy planning computed tomography

• João Otávio Bandeira Diniz UFMA
• Aristófanes Corrêa Silva UFMA
• Anselmo Cardoso de Paiva UFMA

DOI: https://doi.org/10.5753/sibgrapi.est.2021.20009

Resumo

Organs at Risk (OARs) are healthy tissues around cancer that must be preserved in radiotherapy (RT). The spinal cord and esophagus are crucial OARs. In this work, we proposed methods for the segmentation of these OARs from the CT using image processing techniques and deep convolutional neural network (CNN). For spinal cord segmentation, two methods are proposed, the first using techniques such as template matching, superpixel, and CNN. The second method, use adaptive template matching and CNN. In the esophagus segmentation, we proposed a method composed of registration techniques, atlas, pre-processing, U-Net, and post-processing. The methods were applied to 36 planning CT images provided by The Cancer Imaging Archive. The first method for spinal cord segmentation obtained 78.20% Dice. The second method for spinal cord segmentation obtained 81.69% Dice. The esophagus segmentation method obtained an accuracy of 82.15% Dice.

Referências

E. Evans and J. Staffurth, “Principles of cancer treatment by radiotherapy,” Surgery-Oxford International Edition, vol. 36, no. 3, pp. 111–116, 2018.

M. Button and J. Staffurth, “Clinical application of image-guided radiotherapy in bladder and prostate cancer,” Clinical Oncology, vol. 22, no. 8, pp. 698–706, 2010.

B. N. Ruppert, J. M. Watkins, K. Shirai, A. E. Wahlquist, E. Garrett- Mayer, E. G. Aguero, C. A. Sherman, C. E. Reed, and A. K. Sharma, “Cisplatin/irinotecan versus carboplatin/paclitaxel as definitive chemoradiotherapy for locoregionally advanced esophageal cancer,” American journal of clinical oncology, vol. 33, no. 4, pp. 346–352, 2010.

M. Chen, A. Carass, J. Oh, G. Nair, D. L. Pham, D. S. Reich, and J. L. Prince, “Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view,” Neuroimage, vol. 83, pp. 1051–1062, 2013.

J. Yang, H. Veeraraghavan, S. G. Armato III, K. Farahani, J. S. Kirby, J. Kalpathy-Kramer, W. van Elmpt, A. Dekker, X. Han, X. Feng et al., “Autosegmentation for thoracic radiation treatment planning: a grand challenge at aapm 2017,” Medical physics, vol. 45, no. 10, pp. 4568– 4581, 2018.

J. O. B. Diniz, P. H. B. Diniz, T. L. A. Valente, A. C. Silva, and A. C. Paiva, “Spinal cord detection in planning ct for radiotherapy through adaptive template matching, imslic and convolutional neural networks,” Computer methods and programs in biomedicine, vol. 170, pp. 53–67, 2019.

J. Diniz, J. Ferrreira, P. Diniz, B. Serejo, N. Ribeiro, O. Santos, A. Silva, and A. Paiva, “Automatic spinal cord segmentation as organ at risk in planning ct using adaptive template matching and u-net,” in Anais da VII Escola Regional de Computação Aplicada à Saúde. Porto Alegre, RS, Brasil: SBC, 2019, pp. 151–156. [Online]. Available: https://sol.sbc.org.br/index.php/ercas/article/view/9051

——, “Spinal cord segmentation as oar in planning ct for radiotherapy using histogram matching, template matching, and u-net,” Revista de Sistemas e Computação, 2020.

T. Fechter, S. Adebahr, D. Baltas, I. B. Ayed, C. Desrosiers, and J. Dolz, “Esophagus segmentation in ct via 3d fully convolutional neural network and random walk,” Medical physics, vol. 44, no. 12, pp. 6341–6352, 2017.

J. O. B. Diniz, J. L. Ferreira, P. H. B. Diniz, A. C. Silva, and A. C. de Paiva, “Esophagus segmentation from planning ct images using an atlas-based deep learning approach,” Computer Methods and Programs in Biomedicine, vol. 197, p. 105685, 2020.

J. O. Diniz, D. B. Quintanilha, A. C. S. Neto, G. L. da Silva, J. L. Ferreira, S. M. Netto, J. D. Araújo, L. B. Da Cruz, T. F. Silva, C. M. d. S. Martins et al., “Segmentation and quantification of covid-19 infections in ct using pulmonary vessels extraction and deep learning,” Multimedia Tools and Applications, pp. 1–33, 2021.

J. Diniz, J. Ferreira, G. Silva, D. Quintanilha, A. Silva, and A. Paiva, “Segmentação de coração em tomografias computadorizadas utilizando atlas probabilístico e redes neurais convolucionais,” in Anais do XXI Simpósio Brasileiro de Computação Aplicada à Saúde. Porto Alegre, RS, Brasil: SBC, 2021, pp. 83–94. [Online]. Available: https://sol.sbc.org.br/index.php/sbcas/article/view/16055

S. Banik, R. M. Rangayyan, and G. S. Boag, “Automatic segmentation of the ribs, the vertebral column, and the spinal canal in pediatric computed tomographic images,” Journal of digital imaging, vol. 23, no. 3, pp. 301– 322, 2010.

M. La Macchia, F. Fellin, M. Amichetti, M. Cianchetti, S. Gianolini, V. Paola, A. J. Lomax, and L. Widesott, “Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer,” Radiation Oncology, vol. 7, no. 1, p. 160, 2012.

B. De Leener, J. Cohen-Adad, and S. Kadoury, “Automatic segmentation of the spinal cord and spinal canal coupled with vertebral labeling,” IEEE transactions on medical imaging, vol. 34, no. 8, pp. 1705–1718, 2015.

R. F. Verhaart, V. Fortunati, G. M. Verduijn, T. van Walsum, J. F. Veenland, and M. M. Paulides, “Ct-based patient modeling for head and neck hyperthermia treatment planning: Manual versus automatic normaltissue- segmentation,” Radiotherapy and Oncology, vol. 111, no. 1, pp. 158–163, 2014.

B. Ibragimov and L. Xing, “Segmentation of organs-at-risks in head and neck ct images using convolutional neural networks,” Medical physics, vol. 44, no. 2, pp. 547–557, 2017.

X. Dong, Y. Lei, T. Wang, M. Thomas, L. Tang, W. J. Curran, T. Liu, and X. Yang, “Automatic multiorgan segmentation in thorax ct images using u-net-gan,” Medical physics, vol. 46, no. 5, pp. 2157–2168, 2019.

Z. Liu, X. Liu, B. Xiao, S. Wang, Z. Miao, Y. Sun, and F. Zhang, “Segmentation of organs-at-risk in cervical cancer ct images with a convolutional neural network,” Physica Medica, vol. 69, pp. 184–191, 2020.

J. Feulner, S. K. Zhou, M. Hammon, S. Seifert, M. Huber, D. Comaniciu, J. Hornegger, and A. Cavallaro, “A probabilistic model for automatic segmentation of the esophagus in 3-d ct scans,” IEEE transactions on medical imaging, vol. 30, no. 6, pp. 1252–1264, 2011.

D. Grosgeorge, C. Petitjean, B. Dubray, and S. Ruan, “Esophagus segmentation from 3d ct data using skeleton prior-based graph cut,” Computational and mathematical methods in medicine, vol. 2013, 2013.

M. Larsson, Y. Zhang, and F. Kahl, “Robust abdominal organ segmentation using regional convolutional neural networks,” in Scandinavian Conference on Image Analysis. Springer, 2017, pp. 41–52.

R. Trullo, C. Petitjean, S. Ruan, B. Dubray, D. Nie, and D. Shen, “Segmentation of organs at risk in thoracic ct images using a sharpmask architecture and conditional random fields,” in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE, 2017, pp. 1003–1006.

R. Trullo, C. Petitjean, D. Nie, D. Shen, and S. Ruan, “Fully automated esophagus segmentation with a hierarchical deep learning approach,” in 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, 2017, pp. 503–506.

S. Chen, H. Yang, J. Fu, W. Mei, S. Ren, Y. Liu, Z. Zhu, L. Liu, H. Li, and H. Chen, “U-net plus: Deep semantic segmentation for esophagus and esophageal cancer in computed tomography images,” IEEE Access, vol. 7, pp. 82 867–82 877, 2019.

X. Feng, K. Qing, N. J. Tustison, C. H. Meyer, and Q. Chen, “Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3d images,” Medical physics, vol. 46, no. 5, pp. 2169– 2180, 2019.

X. Lou, Y. Zhu, K. Punithakumar, L. H. Le, and B. Li, “Esophagus segmentation in computed tomography images using a u-net neural network with a semiautomatic labeling method,” IEEE Access, vol. 8, pp. 202 459–202 468, 2020.

J. O. B. Diniz, P. H. B. Diniz, T. L. A. Valente, A. C. Silva, A. C. de Paiva, and M. Gattass, “Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks,” Computer Methods and Programs in Biomedicine, vol. 156, pp. 191–207, 2018.

W. Hsu, C. Baumgartner, T. Deserno et al., “Advancing artificial intelligence in sensors, signals, and imaging informatics,” Yearbook of medical informatics, vol. 28, no. 01, pp. 115–117, 2019.