Clinical segmentation for improved pancreatic ductal adenocarcinoma detection and segmentation

The development of Artificial Intelligence (AI) for detection and characterization of Pancreatic Ductal Adenocarcinoma (PDAC) is a challenging task, since PDAC data is scarce compared to data of other types of cancer. However, due to the high mortality rate of the disease, early detection is crucial. For this reason, recent work has focused on exploiting indirect pathological features, e.g. dilated bile ducts due to tumor involvement, as an additional input for supportive algorithms. However, the presented methods require manual annotations of several structures in a CT volume, which is a cumbersome task and not feasible in clinical practice. Therefore, this work investigates the automated segmentation of bile ducts to facilitate improved tumor detection by such methods. Using a coarse-to-fine segmentation architecture, the pancreas, pancreatic duct and the common bile duct are segmented from~3D~CT-scans. The resulting yet individual segmentations form a primary stage, of which the outputs are supplied as input to a secondary pre-trained U-Net-based PDAC detection algorithm, to ultimately detect tumors. We evaluate the performance of the proposed primary segmentation and secondary detection models on a publicly available test set in terms of mean Dice Similarity Coefficient (DSC). The pancreas, common bile duct and pancreatic duct are segmented with a mean DSC of~0.86,~0.69 and~0.57, respectively. With these segmentations as input, a tumor detection sensitivity of~100$\%$ is maintained for the tumor detection model. This continuously high detection sensitivity for tumor detection is comparable to the tumor detection score achieved by using manually annotated structures. This study highlights the benefit of primarily segmenting relevant structures, to use as input for a secondary model for final PDAC detection