Pancreatic duct centerline extraction for image unfolding in photon-counting CT

Pancreatic diseases are often only diagnosed at a late stage, and pancreatic cancer is the most feared due to a very high mortality. Abnormalities of the main pancreatic duct, such as blockages and dilatation, are often (early) signs of such pancreatic diseases, but are difficult to detect in standard Computed Tomography image series. Photon-Counting Computed Tomography with its higher resolution improves the detectability of this duct, allowing diagnostic assessment. A comprehensive visualization in a single view requires a centerline-based unfolding of the duct and pancreas. However, manual centerline annotation is tedious. To automate this process, we introduce a fully automated pipeline for pancreatic duct unfolding by robustly extracting the centerline using Dijkstra’s algorithm on a cost map derived from a segmentation probability map. The core contribution of this work lies in the processing of the data-driven cost map leading to a consistent centerline for generating CPR visualizations of the pancreas. To improve individual steps within the pipeline, we investigate further enhancements such as segmentation filtering and the topology-preserving skeleton recall loss. In the evaluation, we assess performance of our method on both ultra-high-resolution and regular PCCT images. We find that the centerline can be consistently extracted from both scan types, where the centerlines from the ultra-high resolution images exhibit a slightly lower median error of 0.58 mm compared to the 0.73 mm using the regular resolution. • Fully automated pipeline for centerline extraction of the main pancreatic duct. • Utilizes photon-counting CT (PCCT) imaging with enhanced spatial resolution and contrast-to-noise Ratio for improved visibility of fine anatomical structures. • Enables curved planar reformation (CPR) visualization of the pancreas and MPD using a continuous centerline. • Incorporates connected component analysis to filter and refine MPD segmentations. • Investigates the effect of skeleton recall loss on segmentation performance for the tubular-structured MPD and its impact on the overall pipeline.