Towards an Automatic Lung Cancer Screening System in Low Dose Computed Tomography
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We propose a deep learning-based pipeline that, given a low-dose computed tomography of a patient chest, recommends if a patient should be submitted to further lung cancer assessment. The algorithm is composed of a nodule detection block that uses the object detection framework YOLOv2, followed by a U-Net based segmentation. The found structures of interest are then characterized in terms of diameter and texture to produce a final referral recommendation according to the National Lung Screen Trial (NLST) criteria. Our method is trained using the public LUNA16 and LIDC-IDRI datasets and tested on an independent dataset composed of 500 scans from the Kaggle DSB 2017 challenge. The proposed system achieves a patient-wise recall of 89% while providing an explanation to the referral decision and thus may serve as a second opinion tool to speed-up and improve lung cancer screening.
KeywordsComputer aided diagnosis Lung cancer Low dose computed tomography images Screening Deep learning
Guilherme Aresta is funded by the FCT grant contract SFRH/BD/120435/2016. Teresa Araújo is funded by the FCT grant contract SFRH/BD/122365/2016. This study is associated with project NLST-375 and LNDetector, which is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness - COMPETE 2020 Programme and by the National Fundus through the Portuguese funding agency, FCT - Fundação para a Ciência e Tecnologia within project POCI-01-0145-FEDER-016673.
- 1.Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2018. CA A Cancer J. Clin. 68(1), 7–30 (2018)Google Scholar
- 2.The National Lung Screening Trial Research Team: Reduced lung-cancer mortality with low-dose computed tomographic screening. New England J. Med. 365(5), 395–409 (2011)Google Scholar
- 6.Messay, T., Hardie, R.C., Tuinstra, T.R.: Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the lung image database consortium and image database resource initiative dataset. Med. Image Anal. 22(1), 48–62 (2015)CrossRefGoogle Scholar
- 9.Redmon, J., Farhadi, A.: YOLO9000: Better, faster, stronger. In: Proceedings of 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 6517–6525 (2017)Google Scholar
- 10.Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9. IEEE, 7–12 June 2015Google Scholar
- 12.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_28CrossRefGoogle Scholar