Fully automatic reconstruction of prostate high-dose-rate brachytherapy interstitial needles using two-phase deep learning-based segmentation and object tracking algorithms.

ABSTRACT: The critical aspect of successful brachytherapy (BT) is accurate detection of applicator/needle trajectories, which is an ongoing challenge. This study proposes a two-phase deep learning-based method to automate localization of high-dose-rate (HDR) prostate BT catheters through the patient's CT images. The whole process is divided into two phases using two different deep neural networks. First, BT needles segmentation was accomplished through a pix2pix Generative Adversarial Neural network (pix2pix GAN). Second, a Generic Object Tracking Using Regression Networks (GOTURN) was used to predict the needle trajectories. These models were trained and tested on a clinical prostate BT dataset. Among the total 25 patients, 5 patients that consist of 592 slices was dedicated to testing sets, and th

SUBMITTER: Moradi MM 

PROVIDER: S-EPMC11788795 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature