Development of deep learning-based novel auto-segmentation for the prostatic urethra on planning CT images for prostate cancer radiotherapy.

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Radiological Physics and Technology Pub Date : 2024-12-01 Epub Date: 2024-08-14 DOI:10.1007/s12194-024-00832-8
Hisamichi Takagi, Ken Takeda, Noriyuki Kadoya, Koki Inoue, Shiki Endo, Noriyoshi Takahashi, Takaya Yamamoto, Rei Umezawa, Keiichi Jingu
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引用次数: 0

Abstract

Urinary toxicities are one of the serious complications of radiotherapy for prostate cancer, and dose-volume histogram of prostatic urethra has been associated with such toxicities in previous reports. Previous research has focused on estimating the prostatic urethra, which is difficult to delineate in CT images; however, these studies, which are limited in number, mainly focused on cases undergoing brachytherapy uses low-dose-rate sources and do not involve external beam radiation therapy (EBRT). In this study, we aimed to develop a deep learning-based method of determining the position of the prostatic urethra in patients eligible for EBRT. We used contour data from 430 patients with localized prostate cancer. In all cases, a urethral catheter was placed when planning CT to identify the prostatic urethra. We used 2D and 3D U-Net segmentation models. The input images included the bladder and prostate, while the output images focused on the prostatic urethra. The 2D model determined the prostate's position based on results from both coronal and sagittal directions. Evaluation metrics included the average distance between centerlines. The average centerline distances for the 2D and 3D models were 2.07 ± 0.87 mm and 2.05 ± 0.92 mm, respectively. Increasing the number of cases while maintaining equivalent accuracy as we did in this study suggests the potential for high generalization performance and the feasibility of using deep learning technology for estimating the position of the prostatic urethra.

在前列腺癌放疗计划 CT 图像上开发基于深度学习的新型前列腺尿道自动分割技术。
泌尿系统毒性是前列腺癌放疗的严重并发症之一,在以往的报告中,前列腺尿道的剂量-体积直方图与此类毒性有关。以往的研究主要集中在对前列腺尿道的估算上,因为前列腺尿道在 CT 图像中很难划分;然而,这些研究数量有限,主要集中在使用低剂量率放射源的近距离放射治疗病例中,并不涉及体外射束放射治疗(EBRT)。在本研究中,我们旨在开发一种基于深度学习的方法,用于确定符合 EBRT 患者的前列腺尿道位置。我们使用了 430 名局部前列腺癌患者的轮廓数据。在所有病例中,在规划 CT 时都放置了尿道导管以确定前列腺尿道。我们使用了二维和三维 U-Net 分割模型。输入图像包括膀胱和前列腺,而输出图像则侧重于前列腺尿道。二维模型根据冠状和矢状两个方向的结果确定前列腺的位置。评估指标包括中心线之间的平均距离。二维和三维模型的平均中心线距离分别为 2.07 ± 0.87 毫米和 2.05 ± 0.92 毫米。我们在这项研究中增加了病例数,同时保持了同等的准确性,这表明使用深度学习技术估计前列腺尿道位置具有很高的通用性和可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Radiological Physics and Technology
Radiological Physics and Technology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
3.00
自引率
12.50%
发文量
40
期刊介绍: The purpose of the journal Radiological Physics and Technology is to provide a forum for sharing new knowledge related to research and development in radiological science and technology, including medical physics and radiological technology in diagnostic radiology, nuclear medicine, and radiation therapy among many other radiological disciplines, as well as to contribute to progress and improvement in medical practice and patient health care.
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