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Accurate Model-based Segmentation of Gynecologic Brachytherapy Catheter Collections in MRI-images

机译:基于模型的妇科近距离治疗导管集合在MRI图像中的精确分割

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摘要

The gynecological cancer mortality rate, including cervical, ovarian, vaginal and vulvar cancers, is more than 20,000 annually in the US alone. In many countries, including the US, external-beam radiotherapy followed by high dose rate brachytherapy is the standard-of-care. The superior ability of MR to visualize soft tissue has led to an increase in its usage in planning and delivering brachytherapy treatment. A technical challenge associated with the use of MRI imaging for brachytherapy, in contrast to that of CT imaging, is the visualization of catheters that are used to place radiation sources into cancerous tissue. We describe here a precise, accurate method for achieving catheter segmentation and visualization. The algorithm, with the assistance of manually provided tip locations, performs segmentation using image-features, and is guided by a catheter-specific, estimated mechanical model. A final quality control step removes outliers or conflicting catheter trajectories. The mean Hausdorff error on a 54 patient, 760 catheter reference database was 1.49 mm; 51 of the outliers deviated more than two catheter widths (3.4 mm) from the gold standard, corresponding to catheter identification accuracy of 93% in a Syed-Neblett template. In a multi-user simulation experiment for evaluating RMS precision by simulating varying manually-provided superior tip positions, 3σ maximum errors were 2.44 mm. The average segmentation time for a single catheter was 3 seconds on a standard PC. The segmentation time, accuracy and precision, are promising indicators of the value of this method for clinical translation of MR-guidance in gynecologic brachytherapy and other catheter-based interventional procedures.
机译:仅在美国,妇科癌症死亡率(包括宫颈癌,卵巢癌,阴道癌和外阴癌)每年就超过20,000。在许多国家(包括美国),标准的治疗方法是外照射放射疗法,然后进行高剂量率近距离放射疗法。 MR可视化软组织的卓越能力已导致其在计划和实施近距离放射治疗中的使用增加。与CT成像相反,与将MRI成像用于近距离放射治疗相关的技术挑战是如何可视化导管,该导管用于将放射源放置到癌组织中。我们在这里描述一种实现导管分割和可视化的精确方法。该算法在手动提供的尖端位置的帮助下,使用图像特征执行分割,并由特定于导管的估计机械模型指导。最后的质量控制步骤将消除异常值或冲突的导管轨迹。 54名患者的760个导管参考数据库的平均Hausdorff误差为1.49 mm; 51个离群值偏离黄金标准的导管宽度超过两个(3.4毫米),对应于Syed-Neblett模板中93%的导管识别精度。在通过模拟各种手动提供的上端尖端位置来评估RMS精度的多用户模拟实验中,最大3σ误差为2.44毫米。在标准PC上,单个导管的平均分割时间为3秒。分割时间,准确性和精确度是该方法在妇科近距离放射治疗和其他基于导管的介入治疗中对MR指导进行临床翻译的价值的有希望的指标。

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