Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans

  • Yuyin Zhou
  • Lingxi XieEmail author
  • Elliot K. Fishman
  • Alan L. Yuille
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


Automatic segmentation of an organ and its cystic region is a prerequisite of computer-aided diagnosis. In this paper, we focus on pancreatic cyst segmentation in abdominal CT scan. This task is important and very useful in clinical practice yet challenging due to the low contrast in boundary, the variability in location, shape and the different stages of the pancreatic cancer. Inspired by the high relevance between the location of a pancreas and its cystic region, we introduce extra deep supervision into the segmentation network, so that cyst segmentation can be improved with the help of relatively easier pancreas segmentation. Under a reasonable transformation function, our approach can be factorized into two stages, and each stage can be efficiently optimized via gradient back-propagation throughout the deep networks. We collect a new dataset with 131 pathological samples, which, to the best of our knowledge, is the largest set for pancreatic cyst segmentation. Without human assistance, our approach reports a \(63.44\%\) average accuracy, measured by the Dice-Sørensen coefficient (DSC), which is higher than the number (\(60.46\%\)) without deep supervision.



This work was supported by the Lustgarten Foundation for Pancreatic Cancer Research. We thank Dr. Seyoun Park for enormous help.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yuyin Zhou
    • 1
  • Lingxi Xie
    • 1
    Email author
  • Elliot K. Fishman
    • 2
  • Alan L. Yuille
    • 1
  1. 1.The Johns Hopkins UniversityBaltimoreUSA
  2. 2.The Johns Hopkins University School of MedicineBaltimoreUSA

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