Classification of Pancreatic Cystic Neoplasms Based on Multimodality Images

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


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.



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


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© 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

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