@inproceedings{455e3f1408d04e209e02213809c6a5be,
title = "Deep learning-based multi-catheter reconstruction for MRI-guided HDR prostate brachytherapy",
abstract = "Reconstructing catheters on medical images is a crucial step in high-dose-rate (HDR) brachytherapy for treating prostate cancer. However, manually identify the catheters is labor intensive. With its superior soft-tissue contrast, magnetic resonance imaging (MRI) can provide superior anatomic visualization of prostate gland and its surrounding tissues such as the rectum and the bladder. There is a considerable increase of using MRI-guided HDR prostate brachytherapy over the past decades. By incorporating MRI into prostate brachytherapy procedure, the therapeutic ratio could be improved attribute to MRI's capability of differentiating dominant prostatic lesions. However, it has been realized that challenge remains in recognition of multiple catheters in MRI because catheters used in routine HDR prostate brachytherapy appear dark, thus can be easily confused with blood vessels. In this study, we developed a deep learningbased catheter reconstruction method to tackle the challenge. Particularly, a 3D mask scoring regional convolutional neural network has been implemented to automatically identify all the catheters in MRI that are acquired after catheters insertion during HDR prostate brachytherapy. The network was trained using the paired MR images and binary catheter annotation images offered by experienced medical physicists as ground truth. After the network was trained, the locations, sizes and shapes of all the catheters can be predicted given MR images of a new prostate cancer patient receiving HDR brachytherapy. Quantities including catheter tip and shaft errors were computed to assess our proposed method. Our method detected 164 catheters from 11 patients receiving HDR prostate brachytherapy with a catheter tip error of 0.62±1.83 mm and a catheter shaft error of 0.94±0.52 mm. The proposed multi-catheter reconstruction method has the capability of precisely localizing the tips and shafts of catheters in 3D MRI images of HDR prostate brachytherapy. It paves the way for elevating the quality and treatment outcome of MRI-guided HDR prostate brachytherapy.",
keywords = "Brachytherapy, Catheter reconstruction, Deep learning, MRI, Prostate cancer, Segmentation",
author = "Xianjin Dai and Yang Lei and Yupei Zhang and Tonghe Wang and Curran, {Walter J.} and Pretesh Patel and Tian Liu and Xiaofeng Yang",
note = "Publisher Copyright: {\textcopyright} 2021 SPIE.; Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling ; Conference date: 15-02-2021 Through 19-02-2021",
year = "2021",
doi = "10.1117/12.2581123",
language = "英语",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Linte, {Cristian A.} and Siewerdsen, {Jeffrey H.}",
booktitle = "Medical Imaging 2021",
}
