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.
W. Chen and H. Ji—contributed equally to this work.
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References
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
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)
del Castillo C, F., Warshaw, A.L.: Cystic tumors of the pancreas. Surg. Clin. North Am. 75(5), 1001–16 (1995)
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)
Hutchins, G.F., Draganov, P.V.: Cystic neoplasms of the pancreas: a diagnostic challenge. World J. Gastroenterol. 15(1), 48 (2009)
Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131 (2018)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. Computer Science (2014)
Liu, F., Xie, L., Xia, Y., Fishman, E.K., Yuille, A.L.: Joint shape representation and classification for detecting PDAC. arXiv:1804.10684 (2018)
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)
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)
Roth, H., et al.: Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks. arXiv:1706.07346 (2017)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. Computer Science, pp. 2818–2826 (2015)
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)
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)
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This work is supported by the National Natural Science Foundation of China under Grant 61622207.
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Chen, W. et al. (2018). Classification of Pancreatic Cystic Neoplasms Based on Multimodality Images. In: Shi, Y., Suk, HI., Liu, M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science(), vol 11046. Springer, Cham. https://doi.org/10.1007/978-3-030-00919-9_19
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