School of Biomedical Engineering, The University of Western Ontario, London, Ontario, Canada; Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada.
Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD.
Brachytherapy. 2020 Sep-Oct;19(5):659-668. doi: 10.1016/j.brachy.2020.05.006. Epub 2020 Jul 3.
The purpose of this study was to evaluate the use of a semiautomatic algorithm to simultaneously segment multiple high-dose-rate (HDR) gynecologic interstitial brachytherapy (ISBT) needles in three-dimensional (3D) transvaginal ultrasound (TVUS) images, with the aim of providing a clinically useful tool for intraoperative implant assessment.
A needle segmentation algorithm previously developed for HDR prostate brachytherapy was adapted and extended to 3D TVUS images from gynecologic ISBT patients with vaginal tumors. Two patients were used for refining/validating the modified algorithm and five patients (8-12 needles/patient) were reserved as an unseen test data set. The images were filtered to enhance needle edges, using intensity peaks to generate feature points, and leveraged the randomized 3D Hough transform to identify candidate needle trajectories. Algorithmic segmentations were compared against manual segmentations and calculated dwell positions were evaluated.
All 50 test data set needles were successfully segmented with 96% of algorithmically segmented needles having angular differences <3° compared with manually segmented needles and the maximum Euclidean distance was <2.1 mm. The median distance between corresponding dwell positions was 0.77 mm with 86% of needles having maximum differences <3 mm. The mean segmentation time using the algorithm was <30 s/patient.
We successfully segmented multiple needles simultaneously in intraoperative 3D TVUS images from gynecologic HDR-ISBT patients with vaginal tumors and demonstrated the robustness of the algorithmic approach to image artifacts. This method provided accurate segmentations within a clinically efficient timeframe, providing the potential to be translated into intraoperative clinical use for implant assessment.
本研究旨在评估一种半自动算法在三维（3D）经阴道超声（TVUS）图像中同时分割多个高剂量率（HDR）妇科间质近距离放疗（ISBT）针的用途，以期为术中植入物评估提供一种有临床应用价值的工具。
我们对之前开发用于 HDR 前列腺近距离放疗的针分割算法进行了改进和扩展，使其适用于阴道肿瘤的妇科 ISBT 患者的 3D TVUS 图像。两名患者用于改进/验证修改后的算法，五名患者（每例 8-12 根针）保留为未见过的测试数据集。通过使用强度峰值生成特征点来增强针边缘对图像进行滤波，并利用随机 3D Hough 变换来识别候选针轨迹。将算法分割与手动分割进行比较，并评估计算出的驻留位置。
所有 50 根测试数据集针均成功分割，与手动分割针相比，96%的算法分割针的角度差异<3°，最大欧几里得距离<2.1mm。对应驻留位置之间的中位数距离为 0.77mm，86%的针最大差异<3mm。使用该算法的平均分割时间<30s/例。
我们成功地对阴道肿瘤的妇科 HDR-ISBT 患者术中 3D TVUS 图像中的多个针进行了同时分割，并证明了算法方法对图像伪影的稳健性。该方法在临床有效的时间内提供了准确的分割，有可能转化为术中临床应用，用于植入物评估。
