计算机科学
分割
人工智能
胰腺
水平集(数据结构)
图像分割
Sørensen–骰子系数
囊肿
深度学习
胰腺癌
光学(聚焦)
计算机视觉
放射科
模式识别(心理学)
医学
癌症
内科学
物理
光学
作者
Yuyin Zhou,Lingxi Xie,Elliot K. Fishman,Alan Yuille
标识
DOI:10.1007/978-3-319-66179-7_26
摘要
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.
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