Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Med Image Anal. 2011 Apr;15(2):226-37. doi: 10.1016/j.media.2010.10.002. Epub 2010 Oct 26.
In this paper we report and characterize a semi-automatic prostate segmentation method for prostate brachytherapy. Based on anatomical evidence and requirements of the treatment procedure, a warped and tapered ellipsoid was found suitable as the a-priori 3D shape of the prostate. By transforming the acquired endorectal transverse images of the prostate into ellipses, the shape fitting problem was cast into a convex problem which can be solved efficiently. The average whole gland error between non-overlapping volumes created from manual and semi-automatic contours from 21 patients was 6.63 ± 0.9%. For use in brachytherapy treatment planning, the resulting contours were modified, if deemed necessary, by radiation oncologists prior to treatment. The average whole gland volume error between the volumes computed from semi-automatic contours and those computed from modified contours, from 40 patients, was 5.82 ± 4.15%. The amount of bias in the physicians' delineations when given an initial semi-automatic contour was measured by comparing the volume error between 10 prostate volumes computed from manual contours with those of modified contours. This error was found to be 7.25 ± 0.39% for the whole gland. Automatic contouring reduced subjectivity, as evidenced by a decrease in segmentation inter- and intra-observer variability from 4.65% and 5.95% for manual segmentation to 3.04% and 3.48% for semi-automatic segmentation, respectively. We characterized the performance of the method relative to the reference obtained from manual segmentation by using a novel approach that divides the prostate region into nine sectors. We analyzed each sector independently as the requirements for segmentation accuracy depend on which region of the prostate is considered. The measured segmentation time is 14 ± 1s with an additional 32 ± 14s for initialization. By assuming 1-3 min for modification of the contours, if necessary, a total segmentation time of less than 4 min is required, with no additional time required prior to treatment planning. This compares favorably to the 5-15 min manual segmentation time required for experienced individuals. The method is currently used at the British Columbia Cancer Agency (BCCA) Vancouver Cancer Centre as part of the standard treatment routine in low dose rate prostate brachytherapy and is found to be a fast, consistent and accurate tool for the delineation of the prostate gland in ultrasound images.
本文报告并描述了一种用于前列腺近距离治疗的半自动前列腺分割方法。基于解剖学证据和治疗过程的要求,我们发现变形的、变细的椭圆体适合作为前列腺的先验三维形状。通过将获得的前列腺腔内横断面图像转换为椭圆,形状拟合问题被转化为一个可以有效解决的凸问题。21 名患者的手动和半自动轮廓创建的非重叠体积之间的平均整个腺体误差为 6.63±0.9%。为了在近距离治疗计划中使用,在治疗前,如果需要,由放射肿瘤学家对生成的轮廓进行修改。40 名患者的半自动轮廓计算的整个腺体体积误差和修改后的轮廓计算的整个腺体体积误差平均值为 5.82±4.15%。通过比较从 10 个手动轮廓计算的体积与从修改后的轮廓计算的体积之间的体积误差,测量了当提供初始半自动轮廓时医生勾画的偏差量。发现整个腺体的误差为 7.25±0.39%。自动勾画减少了主观性,这体现在手动分割的分割内和分割间观察者变异性分别从 4.65%和 5.95%降低到半自动分割的 3.04%和 3.48%。我们通过使用一种新的方法,将前列腺区域分为九个扇区,来描述该方法相对于手动分割获得的参考的性能。我们分别独立地分析每个扇区,因为分割准确性的要求取决于考虑的前列腺区域。测量的分割时间为 14±1s,初始化额外需要 32±14s。假设如果需要修改轮廓,则需要 1-3 分钟,总分割时间不到 4 分钟,在进行治疗计划之前不需要额外的时间。这与经验丰富的个人所需的 5-15 分钟手动分割时间相比具有优势。该方法目前在不列颠哥伦比亚癌症研究所(BCCA)温哥华癌症中心作为低剂量率前列腺近距离治疗标准治疗常规的一部分使用,被发现是一种快速、一致和准确的工具,用于在超声图像中勾画前列腺。