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

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 the rest were used as train/validation set. The total number of needles in these slices of CT images was 8764, of which the employed pix2pix network was able to segment 98.72 % (8652 of total). Dice Similarity Coefficient (DSC) and IoU (Intersection over Union) between the network output and the ground truth were 0.95 and 0.90, respectively. Moreover, the F1-score, recall, and precision results were 0.95, 0.93, and 0.97, respectively. Regarding the location of the shafts, the proposed model has an error of 0.41 mm. The current study proposed a novel methodology to automatically localize and reconstruct the prostate HDR-BT interstitial needles through the 3D CT images. The presented method can be utilized as a computer-aided module in clinical applications to automatically detect and delineate the multi-catheters, potentially enhancing the treatment quality.