Artificial Intelligence-Assisted Automated Applicator Digitization for Fully-Automated Gynecological High-Dose Rate Brachytherapy Treatment Planning

Abstract

To automate the digitization of plastic and titanium applicators used in interstitial and hybrid gynecological (GYN) computed tomography (CT)-based high-dose-rate (HDR) brachytherapy procedures to accelerate the planning and reduce the potential for planning errors. Our hypothesis is that artificial intelligence can accurately automate the identification and digitization of plastic and titanium applicators used in HDR brachytherapy. Forty-eight patients who had received GYN procedures (7 tandem/ring: plastic applicators, 41 interstitial: titanium needles) were selected retrospectively. Patients were randomly split into training (n = 40) and test (n = 8) sets for this study. DICOM images and digitized needles from delivered plans were converted to 3D binary format. The points from each needle were transformed to individual contours and combined into a single binary mask using custom software. Using nnU-Net, a self-configuring deep convolutional neural network, 2D and 3D U-Net architectures were trained and ensembled. With the CT image as input, the nnU-Net model learned features to automatically segment the needle contours. Lastly, a 3D U-Net model was trained using 5 of the 7 tandem/ring cases (plastic applicators), with two reserved to evaluate this automated digitization. The models' performance was evaluated using the Dice Similarity Coefficient (DSC) and identification rate for individual needles. The model trained on 40 patients performed well on titanium needle cases [mean (+/- std. dev.) DSC = 0.738+/-0.034], but did not perform well on the tandem/ring cases [DSC = 0.408] in the test set. This model automatically identified 100% (54 out of 54) titanium needles but missed all plastic applicators from tandem/ring cases. Training a model with only a limited number of tandem/ring (plastic applicators) cases greatly improved segmentation accuracy [mean DSC = 0.646] for tandem/ring test cases. This model which was trained using only tandem/ring cases, automatically identified 7 out of 7 needles (100% vs 0% with previous model) from cases in the test set. The nnU-Net can automatically detect HDR needles with high confidence. Using applicator-specific identification models may improve digitization accuracy. Further evaluation of these tools on larger datasets will confirm the findings of this study.