Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans
This work addresses a challenging medical imaging problem for clinical diagnosis, but it is incremental as it builds on existing segmentation methods with a specific enhancement.
The paper tackled pancreatic cyst segmentation in abdominal CT scans by introducing deep supervision to leverage pancreas segmentation, achieving a 63.44% Dice-Sørensen coefficient, which is higher than the 60.46% baseline without deep supervision.
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.