Using a deep learning approach for implanted seed detection on fluoroscopy images in prostate brachytherapy.

Abstract

Purpose: To apply a deep learning approach to automatically detect implanted seeds on a fluoroscopy image in prostate brachytherapy.

Material and methods: Forty-eight fluoroscopy images of patients, who underwent permanent seed implant (PSI) were used for this study after our Institutional Review Boards approval. Pre-processing procedures that were used to prepare for the training data, included encapsulating each seed in a bounding box, re-normalizing seed dimension, cropping to a region of prostate, and converting fluoroscopy image to PNG format. We employed a pre-trained faster region convolutional neural network (R-CNN) from PyTorch library for automatic seed detection, and leave-one-out cross-validation (LOOCV) procedure was applied to evaluate the performance of the model.

Results: Almost all cases had mean average precision (mAP) greater than 0.91, with most cases (83.3%) having a mean average recall (mAR) above 0.9. All cases achieved F1-scores exceeding 0.91. The averaged results for all the cases were 0.979, 0.937, and 0.957 for mAP, mAR, and F1-score, respectively.

Conclusions: Although there are limitations shown in interpreting overlapping seeds, our model is reasonably accurate and shows potential for further applications.