Physicians use endoscopic procedures to diagnose and treat a variety of medical conditions. For example, bronchoscopy is often performed to diagnose lung cancer. The current practice for planning endoscopic procedures requires the physician to manually scroll through the slices of a three-dimensional (3D) medical image. When doing this scrolling, the physician must perform 3D mental reconstruction of the endoscopic route to reach a specific diagnostic region of interest (ROI). Unfortunately, in the case of complex branching structures such as the airway tree, ROIs are often situated several generations away from the organ's origin. Existing image-analysis methods can help define possible endoscopic navigation paths, but they do not provide specific routes for reaching a given ROI. We have developed an automated method to find a specific route to reach an ROI. Given a 3D medical image, our method takes as inputs: (1) pre-defined ROIs; (2) a segmentation of the branching organ through which the endoscopic device will navigate; and (3) centerlines (paths) through the segmented organ. We use existing methods for branching-organ segmentation and centerline extraction. Our method then (1) identifies the closest paths (routes) to the ROI; and (2) if necessary, performs a directed search for the organ of interest, extending the existing paths to complete a route. Results from human 3D computed tomography chest images illustrate the efficacy of the method.