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-S{\o}rensen coefficient (DSC), which is higher than the number (60.46%)
without deep supervision.

介绍了一种基于深度学习的胰腺囊肿自动分割方法，通过加入深度监督并基于合理的变换函数，将分割任务分解为不同的步骤，应用于 131 个病理样本中取得了 63.44% 的平均精度（Dice-Sørensen 系数），高于未使用深度学习方法的 60.46%。