Automatic detection of main pancreatic duct dilation and pancreatic parenchymal atrophy based on a shape feature in abdominal contrast-enhanced CT images.

Abstract

Purpose: The purpose of this study was to develop and evaluate an algorithm for calculating a shape feature to automatically detect both main pancreatic duct dilation (MPDD) and pancreatic parenchymal atrophy (PPA) in abdominal contrast-enhanced CT (CE-CT) images.

Approach: The proposed algorithm for the automatic detection of MPDD and PPA is composed of five processes: coarse pancreas segmentation, fine pancreas segmentation, main pancreatic duct (MPD) segmentation, centerline estimation, and shape feature calculation. First, the pancreas region is segmented by a deep learning convolutional neural network (CNN). Then, the MPD region is segmented inside the pancreatic region by the deep learning CNN. Next, centerline estimation is performed using Dijkstra's rooting algorithm. Finally, in shape feature calculation, the cross-sectional area ratio of the pancreatic duct to the pancreatic parenchyma (DP ratio) is calculated in all cross sections perpendicular to the identified centerline, and the 90th percentile value of the DP ratio for all cross sections (90th DP ratio) is calculated. The detection performance of the 90th DP ratio for MPDD and PPA was evaluated using 56 abdominal CE-CT images available as public data.

Results: The average of the 90th DP ratio was 0.059 in 48 cases with MPDD and 0.007 in eight cases without MPDD ( $p<0.001$ ) and 0.074 in 31 cases with PPA and 0.023 in 25 cases without PPA ( $p<0.001$ ).

Conclusions: We have developed an algorithm for calculating an automatically measurable shape feature called the 90th DP ratio for the detection of MPDD and PPA.