Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks

TL;DR

This paper introduces a 3D fully convolutional network architecture for automated pancreas segmentation in CT scans, achieving state-of-the-art accuracy on a clinical dataset with significant shape variability.

Contribution

The work presents a novel 3D FCN with skip connections for pancreas segmentation, demonstrating improved performance over previous methods.

Findings

Achieved an average Dice score of 89.7% on test data.

Outperformed existing methods on the same dataset.

Validated the effectiveness of summation skip connections.

Abstract

Pancreas segmentation in computed tomography imaging has been historically difficult for automated methods because of the large shape and size variations between patients. In this work, we describe a custom-build 3D fully convolutional network (FCN) that can process a 3D image including the whole pancreas and produce an automatic segmentation. We investigate two variations of the 3D FCN architecture; one with concatenation and one with summation skip connections to the decoder part of the network. We evaluate our methods on a dataset from a clinical trial with gastric cancer patients, including 147 contrast enhanced abdominal CT scans acquired in the portal venous phase. Using the summation architecture, we achieve an average Dice score of 89.7 $\pm$ 3.8 (range [79.8, 94.8]) % in testing, achieving the new state-of-the-art performance in pancreas segmentation on this dataset.