Purpose/Objective(s): Needle reconstruction is a crucial step in intracavitary-interstitial brachytherapy (IC-ISBT). Currently, needle reconstruction relies on manual processes, which are time-consuming and prone to errors. Automated segmentation and reconstruction methods for interstitial needles can potentially improve efficiency and reduce clinical workload. The objective of this study is to utilize existing radiation therapy plans to achieve deep learning-based reconstruction of interstitial needles in computed tomography (CT) images of cervical cancer brachytherapy.