Fully Automatic Reconstruction of Prostate High Dose Rate Brachytherapy Interstitial Needles by Using Two Phases Deep Learning Based Segmentation and Object Tracking Algorithms

Abstract

The essential step of successful brachytherapy would be precise applicator/needles trajectory detection, which is an open problem yet. This study proposes a two-phase deep learning-based method to automate the localization of high-dose-rate (HDR) prostate brachytherapy catheters through the patient's CT images. The whole process is divided into two phases using two different deep neural networks. First, brachytherapy needles segmentation is 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 brachytherapy dataset. Among the total 25 patients, 5 patients that consisted 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 is 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 brachytherapy 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.

Automatic reconstruction of interstitial needles using CT images in post-operative cervical cancer brachytherapy based on deep learning

“…Two phases DL-based segmentation and object-tracking algorithms were adopted to reconstruct the interstitial needles in CT-guided prostate brachytherapy. In a study [34], DSC between the network output and the ground truth was 0.95. In the present work, the nnU-Net model was trained and reconstructed the metallic needles with average DSC value of 0.89, and 95% HD value of 0.74 mm based on CT images.…”

Deep Learning-based Non-rigid Image Registration for High-dose Rate Brachytherapy in Inter-fraction Cervical Cancer

“…Recently, deep learning approaches, including supervised, unsupervised, and semi-supervised methods, are employed in different medical image analysis tasks [18][19][20][21][22][23][24][25][26][27][28], different BT tasks [29][30][31], and learn and carry out spatial alignment/ transformation between images [32][33][34][35][36][37][38][39][40][41]. These methods usually used convolutional neural networks (CNNs) to extract informative features automatically to perform this task [32][33][34][35][36][37][38][39][40][41].…”