Bronchoscopy is a crucial step in lung cancer diagnosis after observing a nodule in a lung computed tomography (CT) scan. However, it is often a difficult procedure, particularly for less experienced clinicians. We propose a pipeline for the automatic detection of bronchoscopes in x-ray images to support clinicians in lung airway navigation. Segmentation of bronchoscopes, like other tubular structures, is inherently difficult due to their narrowshape and poor image contrast. Our approach employs two preprocessing strategies, contrast limited adaptive histogram equalization (CLAHE) and negative log (NegLog) transform, along with two loss functions: mean squared Error (MSE) and weighted MSE (WMSE), with MSE serving as the baseline. We evaluate on two different datasets, and the model using WMSE as loss function along with CLAHE as preprocessing can predict 99% of the test images with 0.68 mean Dice similarity coefficient (DSC), 0.63 centerline Dice and 3 mm pixel distance. Additionally, we apply random sample consensus (RANSAC) and skeletonization algorithms for postprocessing and polyline retrieval and our findings indicate that skeletonization offers a more robust solution for extracting polylines from tubular structures compared to RANSAC.