Abstract
We aim to detect pancreatic ductal adenocarcinoma (PDAC) in abdominal CT scans, which sheds light on early diagnosis of pancreatic cancer. This is a 3D volume classification task with little training data. We propose a two-stage framework, which first segments the pancreas into a binary mask, then compresses the mask into a shape vector and performs abnormality classification. Shape representation and classification are performed in a joint manner, both to exploit the knowledge that PDAC often changes the shape of the pancreas and to prevent over-fitting. Experiments are performed on 300 normal scans and 136 PDAC cases. We achieve a specificity of \(90.2\%\) (false alarm occurs on less than 1/10 normal cases) at a sensitivity of \(80.2\%\) (less than 1/5 PDAC cases are not detected), which show promise for clinical applications.
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- 1.
Throughout this paper, an abnormal pancreas is defined as one suffering from PDAC.
- 2.
To make our approach generalized, we do not assume the tumors are annotated in the training set, and so we do not perform tumor segmentation.
- 3.
The early diagnosis of PDAC is difficult and can be uncertain from CT scans. In our case, the radiologists proved these PDAC cases with biopsy checks. They can easily miss some of these cases if they were not told their abnormality beforehand.
References
Brock, A., Lim, T., Ritchie, J.M., Weston, N.: Generative and discriminative voxel modeling with convolutional neural networks. arXiv:1608.04236 (2016)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)
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®) (2017)
Rolfe, J.T., LeCun, Y.: Discriminative recurrent sparse auto-encoders. arXiv:1301.3775 (2013)
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
Stewart, B.W.K.P., Wild, C.P., et al.: World cancer report 2014. Health (2017)
Yu, Q., Xie, L., Wang, Y., Zhou, Y., Fishman, E.K., Yuille, A.L.: Recurrent saliency transformation network: incorporating multi-stage visual cues for small organ segmentation. arXiv:1709.04518 (2017)
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
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Liu, F., Xie, L., Xia, Y., Fishman, E., Yuille, A. (2019). Joint Shape Representation and Classification for Detecting PDAC. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_25
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DOI: https://doi.org/10.1007/978-3-030-32692-0_25
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