Approaching automated applicator digitization from a new angle: Using sagittal images to improve deep learning accuracy and robustness in high-dose-rate prostate brachytherapy

Abstract

PURPOSETo automate the segmentation of treatment applicators on computed tomography (CT) images for high-dose-rate (HDR) brachytherapy prostate patients implanted with titanium needles with the goals of improving plan quality and reducing the patient's time under anesthesia. METHODSThe investigation was performed using 57 retrospective, interstitial prostate treatments randomly assigned to training (n = 27), validation (n = 10), and testing (n = 20). Unique to this work, the CT image set was reformatted into 2D sagittal slices instead of the default axial orientation. A deep learning-based segmentation was performed using a 2D U-Net architecture followed by a density-based linkage clustering algorithm to classify individual catheters in 3D. Potential confounders, such as gold seeds and conjoined applicators with intersecting needle geometries, were corrected using a customized polynomial fitting algorithm. The geometric agreement of the automated digitization was evaluated against the clinically treated manual digitization to measure tip and shaft errors in the reconstruction. RESULTSThe proposed algorithm achieved tip and shaft agreements of -0.1 ± 0.6 mm (range -1.8 mm to 1.4 mm) and 0.13 ± 0.09 mm (maximum 0.96 mm), respectively on a data set with 20 patients and 353 total needles. Our method was able to separate all intersecting applicators reliably. The time to generate the automated applicator digitization averaged approximately 1 min. CONCLUSIONSUsing sagittal instead of axial images for 2D segmentation of interstitial brachytherapy applicators produced submillimeter agreement with manual segmentation. The automated digitization of interstitial applicators in prostate brachytherapy has the potential to improve quality and consistency while reducing the patient's time under anesthesia.