Centerline-guided reinforcement learning model for pancreatic duct identifications.

Purpose: Pancreatic ductal adenocarcinoma is forecast to become the second most significant cause of cancer mortality as the number of patients with cancer in the main duct of the pancreas grows, and measurement of the pancreatic duct diameter from medical images has been identified as relevant for its early diagnosis. Approach: We propose an automated pancreatic duct centerline tracing method from computed tomography (CT) images that is based on deep reinforcement learning, which employs an artificial agent to interact with the environment and calculates rewards by combining the distances from the target and the centerline. A deep neural network is implemented to forecast step-wise values for each potential action. With the help of this mechanism, the agent can probe along the pancreatic duct centerline using the best possible navigational path. To enhance the tracing accuracy, we employ landmark-based registration, which enables the generation of a probability map of the pancreatic duct. Subsequently, we utilize a gradient-based method on the registered data to extract a probability map specifically indicating the centerline of the pancreatic duct. Results: Three datasets with a total of 115 CT images were used to evaluate the proposed method. Using image hold-out from the first two datasets, the method performance was 2.0, 4.0, and 2.1 mm measured in terms of the mean detection error, Hausdorff distance (HD), and root mean squared error (RMSE), respectively. Using the first two datasets for training and the third one for testing, the method accuracy was 2.2, 4.9, and 2.6 mm measured in terms of the mean detection error, HD, and RMSE, respectively. Conclusions: We present an algorithm for automated pancreatic duct centerline tracing using deep reinforcement learning. We observe that validation on an external dataset confirms the potential for practical utilization of the presented method.