Advertisement

Classification of Pancreatic Cystic Neoplasms Based on Multimodality Images

  • Weixiang Chen
  • Hongchen Ji
  • Jianjiang Feng
  • Rong Liu
  • Yi Yu
  • Ruiquan Zhou
  • Jie Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)

Abstract

Classification of pancreatic cystic neoplasms (PCN) into subclasses is crucial since their treatments are different. However, accurate classification is very difficult even for radiologists, due to similar appearance and shape. We propose a network called PCN-Net which makes use of T1/T2 MRI of abdomen by its three stages design. The first and second stages are trained on T1 and T2 separately for detection and inter-modality registration. After a Z-Continuity Filter and modalities fusion, the third stage predict the results with registered image pairs. On a database of 48 patients, our method can predict with slice level accuracy of \(80.0\%\) and patient level accuracy of \(92.3\%\), which are much better than other baseline methods.

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant 61622207.

References

  1. 1.
    Abadi, M., Agarwal, A., Barham, P., Brevdo, E., et al.: TensorFlow: large-scale machine learning on heterogeneous systems. arXiv:1603.04467 (2016). Software available from https://tensorflow.org
  2. 2.
    Cai, J., Lu, L., Xing, F., Yang, L.: Pancreas segmentation in CT and MRI images via domain specific network designing and recurrent neural contextual learning. arXiv:1803.11303 (2018)
  3. 3.
    del Castillo C, F., Warshaw, A.L.: Cystic tumors of the pancreas. Surg. Clin. North Am. 75(5), 1001–16 (1995)CrossRefGoogle Scholar
  4. 4.
    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)
  5. 5.
    Hutchins, G.F., Draganov, P.V.: Cystic neoplasms of the pancreas: a diagnostic challenge. World J. Gastroenterol. 15(1), 48 (2009)CrossRefGoogle Scholar
  6. 6.
    Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131 (2018)CrossRefGoogle Scholar
  7. 7.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. Computer Science (2014)Google Scholar
  8. 8.
    Liu, F., Xie, L., Xia, Y., Fishman, E.K., Yuille, A.L.: Joint shape representation and classification for detecting PDAC. arXiv:1804.10684 (2018)
  9. 9.
    Lu, X., Zhang, S., Ma, C., Peng, C., Lv, Y., Zou, X.: The diagnostic value of eus in pancreatic cystic neoplasms compared with CT and MRI. Endosc. Ultrasound 4(4), 324–329 (2015)CrossRefGoogle Scholar
  10. 10.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp. 91–99 (2015)Google Scholar
  11. 11.
    Roth, H., et al.: Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks. arXiv:1706.07346 (2017)
  12. 12.
    Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. Computer Science, pp. 2818–2826 (2015)Google Scholar
  14. 14.
    Zhou, Y., Xie, L., Fishman, E.K., Yuille, A.L.: Deep supervision for pancreatic cyst segmentation in abdominal CT scans. In: MICCAI, pp. 222–230 (2017)Google Scholar
  15. 15.
    Zhou, Y., Xie, L., Shen, W., Wang, Y., Fishman, E.K., Yuille, A.L.: A fixed-point model for pancreas segmentation in abdominal CT scans. In: MICCAI, pp. 693–701 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of AutomationTsinghua UniversityBeijingChina
  2. 2.State Key Lab of Intelligent Technologies and SystemsTsinghua UniversityBeijingChina
  3. 3.Beijing National Research Center for Information Science and TechnologyBeijingChina
  4. 4.Department of Hepatobiliary and Pancreatic Surgical OncologyChinese PLA General Hospital and Chinese PLA Medical SchoolBeijingChina

Personalised recommendations