Automatic detection and tracking of marker seeds implanted in prostate cancer patients using a deep learning algorithm

Abstract

Purpose: Fiducial marker seeds are often used as a surrogate to identify and track the positioning of prostate volume in the treatment of prostate cancer. Tracking the movement of prostate seeds aids in minimizing the prescription dose spillage outside the target volume to reduce normal tissue complications. In this study, You Only Look Once (YOLO) v2TM (MathWorksTM) convolutional neural network was employed to train ground truth datasets and develop a program in MATLAB that can visualize and detect the seeds on projection images obtained from kilovoltage (kV) X-ray volume imaging (XVI) panel (ElektaTM). Methods: As a proof of concept, a wax phantom containing three gold marker seeds was imaged, and kV XVI seed images were labeled and used as ground truth to train the model. The projection images were corrected for any panel shift using flex map data. Upon successful testing, labeled marker seeds and projection images of three patients were used to train a model to detect fiducial marker seeds. A software program was developed to display the projection images in real-time and predict the seeds using YOLO v2 and determine the centers of the marker seeds on each image. Results: The fiducial marker seeds were successfully detected in 98% of images from all gantry angles; the variation in the position of the seed center was within ± 1 mm. The percentage difference between the ground truth and the detected seeds was within 3%. Conclusion: Our study shows that deep learning can be used to detect fiducial marker seeds in kV images in real time. This is an ongoing study, and work is underway to extend it to other sites for tracking moving structures with minimal effort.

Keywords

• deep learning
• fiducial markers
• organ motion
• prostate cancer
• radiotherapy