Abstract
Early detection of lung cancer increases a patient’s survival rate and provides healthcare professionals, valuable time, and information to administer effective treatment. Lung nodules are early signs of lung cancer. Computer-aided diagnostic systems that can identify pulmonary nodules improve early detection as well as provide an independent second opinion. We propose an automated workflow for follow-up recommendation based on low-dose computed tomography (LDCT) images using deep learning, as per 2017 Fleischner Society guidelines. As per guidelines, follow-up is based on size, volume and texture of nodules. In this paper, we present a 5 stage approach for automated follow-up recommendation. The 5 stages are Lung segmentation, Nodule detection and False Positive Reduction (FPR), Texture classification, Nodule segmentation and Follow-up recommendation. Our nodule detection has a sensitivity of 94% @ 1 false positive per scan. The FPR network improves the specificity of detection to 90% without changing sensitivity. Nodule segmentation has a Jaccard index of 0.77 on 768 nodules from Lung Nodule Database (LNDb) [1]. Texture classification has a sensitivity of 97% on solid nodules and a Fleiss-Cohen’s Kappa of 0.37 on LNDb data with most errors between sub-solid and solid nodules. Our rule-based follow-up recommendation has a Fleiss-Cohen’s Kappa of 0.53 on 236 patients from LNDb. In conclusion, we found that rule-based approach for follow-up alongside deep learning models is the best approach in achieving best results. As we improve the first 4 stages, we foresee that recommendation from AI will become closer to radiologists recommendation.
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References
Pedrosa, J., et al.: LNDb: a lung nodule database on computed tomography. arXiv preprint arXiv:1911.08434 (2019)
Bray, F., et al.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Can. J. Clin. 68, 394–424 (2018). https://doi.org/10.3322/caac.21492
https://www.lung.org/our-initiatives/research/monitoring-trends-in-lung-disease/state-of-lung-cancer
Parikh, J.R., et al.: Radiologist burnout according to surveyed radiology practice leaders. J. Am. Coll. Radiol. 17(1), 78–81 (2020)
Singh, S., et al.: Evaluation of reader variability in the interpretation of follow-up CT scans at lung cancer screening. Radiology 259(1), 263–270 (2011)
Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. J. Am. Med. Assoc. 316(22), 2402–2410 (2016)
Litjens, G., et al.: A survey on deep learning in medical image analysis. arXiv preprint arXiv:1702.05747 (2017)
Ren, S., He, K., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Zagoruyko, S., et al.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)
Armato III, S.G., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)
Holmes III, D., Bartholmai, B., Karwoski, R., Zavaletta, V., Robb, R.: The lung tissue research consortium: an extensive open database containing histological clinical and radiological data to study chronic lung disease. In: MICCAI Open Science Workshop (2006)
National Lung Screening Trial Research Team: Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365(5), 395–409 (2011)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Liao, F., et al.: Evaluate the malignancy of pulmonary nodules using the 3-D deep leaky noisy-or network. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3484–3495 (2019)
Lin, T.Y., et al.: Focal loss for dense object detection. In: Proceedings of the IEEE international Conference on Computer Vision, pp. 2980–2988 (2017)
Çiçek, Ö., Abdulkadir, A., Lienkamp, Soeren S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, Mert R., Unal, G., Wells, W. (eds.) MICCAI 2016, Part II. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
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Kaluva, K.C., Vaidhya, K., Chunduru, A., Tarai, S., Nadimpalli, S.P.P., Vaidya, S. (2020). An Automated Workflow for Lung Nodule Follow-Up Recommendation Using Deep Learning. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_32
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DOI: https://doi.org/10.1007/978-3-030-50516-5_32
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