Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans

TL;DR

This paper proposes a deep supervision approach for pancreatic cyst segmentation in abdominal CT scans, improving accuracy by leveraging easier pancreas segmentation to aid cyst detection, validated on a new large dataset.

Contribution

The study introduces a novel deep supervision method that enhances pancreatic cyst segmentation by utilizing pancreas localization, and provides a new large dataset for this task.

Findings

Achieved 63.44% DSC accuracy, surpassing previous methods.

Introduced a new dataset with 131 samples for pancreatic cyst segmentation.

Demonstrated the effectiveness of deep supervision in medical image segmentation.

Abstract

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.