Abstract
We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans. Our idea is named multi-scale segmentation-for-classification, which classifies volumes by checking if at least a sufficient number of voxels is segmented as tumors, by which we can provide radiologists with tumor locations. In order to deal with tumors with different scales, we train and test our volumetric segmentation networks with multi-scale inputs in a coarse-to-fine flowchart. A post-processing module is used to filter out outliers and reduce false alarms. We collect a new dataset containing 439 CT scans, in which 136 cases were diagnosed with PDAC and 303 cases are normal, which is the largest set for PDAC tumors to the best of our knowledge. To offer the best trade-off between sensitivity and specificity, our proposed framework reports a sensitivity of \(94.1\%\) at a specificity of \(98.5\%\), which demonstrates the potential to make a clinical impact.
Z. Zhu and Y. Xia—Equally contributed to the work.
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References
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. In: ICLR (2016)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Dou, Q., Chen, H., Yu, L., Qin, J., Heng, P.A.: Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE TBE 64, 1558–1567 (2017)
Hussein, S., Chuquicusma, M.M., Kandel, P., Bolan, C.W., Wallace, M.B., Bagci, U.: Supervised and unsupervised tumor characterization in the deep learning era. arXiv:1801.03230 (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 3DV (2016)
PDQ Adult Treatment Editorial Board: Pancreatic cancer treatment (PDQ®)
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
Roth, H.R., et al.: DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 556–564. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_68
Roth, H.R., Lu, L., Farag, A., Sohn, A., Summers, R.M.: Spatial aggregation of holistically-nested networks for automated pancreas segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 451–459. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_52
Stewart, B.W.K.P., Wild, C.P., et al.: World cancer report 2014. Health (2017)
Xia, Y., Xie, L., Liu, F., Zhu, Z., Fishman, E.K., Yuille, A.L.: Bridging the gap between 2D and 3D organ segmentation with volumetric fusion net. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 445–453. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_51
Zhang, L., Lu, L., Summers, R.M., Kebebew, E., Yao, J.: Personalized pancreatic tumor growth prediction via group learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 424–432. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_48
Zhou, Y., Xie, L., Fishman, E.K., Yuille, A.L.: Deep supervision for pancreatic cyst segmentation in abdominal CT scans. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 222–230. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_26
Zhu, Z., Xia, Y., Shen, W., Fishman, E.K., Yuille, A.L.: A 3D coarse-to-fine framework for volumetric medical image segmentation. In: 3DV (2018)
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This work was supported by the Lustgarten Foundation for Pancreatic Cancer Research.
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Zhu, Z., Xia, Y., Xie, L., Fishman, E.K., Yuille, A.L. (2019). Multi-scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_1
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