PP04 Presentation Time: 4:27 PM

Abstract

Purpose

In the current procedure of high-dose-rate prostate brachytherapy, physicians insert catheters guided by ultrasound in the operating room. Subsequently, CT/MR/ultrasound images are acquired, and manual delineation of target/organs-at-risk is performed for treatment plan optimization. Catheter placement relies on physician experience, lacking feedback on plan quality during the implantation. Sub-optimal catheter implantation may lead to suboptimal plans or additional catheter adjustments requiring additional anesthesia time. In this study, we explored a novel automatic, real-time catheter tracking and target/organ segmentation method, which can be used with the current plan optimization program to potentially provide an instant plan quality feedback permitting physicians to optimize needle placement, and expediting the subsequent planning process.

Materials and Methods

A deep learning neural network was developed to take the last 5 frames of the real-time videos from ultrasound and provide the coordinates of all the catheters it detected, as well as the contours of prostate, rectum and urethra, on the last frame. After the ultrasound probe scanned the entire prostate region, the catheter coordinates on each frame were then fitted to corresponding 3D lines in order to produce the line functions of each catheter in 3D space, as well as the segmented contours of each frame were stacked together. A total of 518 patients who underwent prostate HDR brachytherapy as boost treatment in our clinic were retrospectively investigated, each of which had ultrasound images acquired, contoured and digitized for treatment planning after catheter placement. Among them, 482 patients were used for the training cohort and 36 patients were used for the testing cohort. The median number of catheters per patient was 14.

Results

Among the 477 catheters in the testing patients, the proposed method successfully detected 472 catheters, with an accuracy of 99.0%. The average displacement between the detected catheters and the ground truth catheters on 2D ultrasound images is 0.63±0.55 mm. The mean Dice score for prostate segmentation is 0.90±0.08. The maximum distance of rectum between ground truth and segmentation is 2.80±1.71 mm on average among all patients. The mean center distance of urethra between ground truth and segmentation is 0.76±0.56 mm. The mean time of processing each frame is 15.54±1.31 ms.

Conclusion

The accuracy and efficiency of the proposed method in tracking catheters and segmenting target and organs have been demonstrated with retrospective ultrasound data. It is seen that the proposed artificial intelligence-based method can facilitate a real-time, US-based automatic treatment planning program for prostate HDR brachytherapy.