Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, 08854, USA.
Acad Radiol. 2011 Jun;18(6):745-54. doi: 10.1016/j.acra.2011.01.016.
Accurate prostate volume estimation is useful for calculating prostate-specific antigen density and in evaluating posttreatment response. In the clinic, prostate volume estimation involves modeling the prostate as an ellipsoid or a spheroid from transrectal ultrasound, or T2-weighted magnetic resonance imaging (MRI). However, this requires some degree of manual intervention, and may not always yield accurate estimates. In this article, we present a multifeature active shape model (MFA) based segmentation scheme for estimating prostate volume from in vivo T2-weighted MRI.
We aim to automatically determine the location of the prostate boundary on in vivo T2-weighted MRI, and subsequently determine the area of the prostate on each slice. The resulting planimetric areas are aggregated to yield the volume of the prostate for a given patient. Using a set of training images, the MFA learns the most discriminating statistical texture descriptors of the prostate boundary via a forward feature selection algorithm. After identification of the optimal image features, the MFA is deformed to accurately fit the prostate border. An expert radiologist segmented the prostate boundary on each slice and the planimetric aggregation of the enclosed areas yielded the ground truth prostate volume estimate. The volume estimation obtained via the MFA was then compared against volume estimations obtained via the ellipsoidal, Myschetzky, and prolated spheroids models.
We evaluated our MFA volume estimation method on a total 45 T2-weighted in vivo MRI studies, corresponding to both 1.5 Tesla and 3.0 Tesla field strengths. The results revealed that the ellipsoidal, Myschetzky, and prolate spheroid models overestimated prostate volumes, with volume fractions of 1.14, 1.53, and 1.96, respectively. By comparison, the MFA yielded a mean volume fraction of 1.05, evaluated using a fivefold cross-validation scheme. A correlation with the ground truth volume estimations showed that the MFA had an r(2) value of 0.82, whereas the clinical volume estimation schemes had a maximum value of 0.70.
Our MFA scheme involves minimal user intervention, is computationally efficient and results in volume estimations more accurate than state of the art clinical models.
准确估计前列腺体积对于计算前列腺特异性抗原密度和评估治疗后反应非常有用。在临床上，前列腺体积估计涉及从经直肠超声或 T2 加权磁共振成像 (MRI) 将前列腺建模为椭球体或球体。然而，这需要一定程度的手动干预，并且并不总是能产生准确的估计。在本文中，我们提出了一种基于多特征主动形状模型 (MFA) 的分割方案，用于从体内 T2 加权 MRI 估计前列腺体积。
我们旨在自动确定体内 T2 加权 MRI 上前列腺边界的位置，然后确定每个切片上前列腺的面积。将得到的平面面积进行汇总，以获得给定患者的前列腺体积。使用一组训练图像，MFA 通过前向特征选择算法学习前列腺边界最具鉴别力的统计纹理描述符。确定最佳图像特征后，MFA 会变形以准确拟合前列腺边界。一位放射科专家在每个切片上分割前列腺边界，然后将封闭区域的平面面积汇总，得出前列腺体积的真实估计值。通过 MFA 获得的体积估计值与通过椭球体、Myschetzky 和长椭球体模型获得的体积估计值进行比较。
我们在总共 45 项体内 T2 加权 MRI 研究中评估了我们的 MFA 体积估计方法，这些研究分别对应于 1.5T 和 3.0T 场强。结果表明，椭球体、Myschetzky 和长椭球体模型高估了前列腺体积，体积分数分别为 1.14、1.53 和 1.96。相比之下，MFA 在五重交叉验证方案中得到的平均体积分数为 1.05。与真实体积估计的相关性表明，MFA 的 r(2) 值为 0.82，而临床体积估计方案的最大值为 0.70。
我们的 MFA 方案涉及最小的用户干预，计算效率高，产生的体积估计比现有的临床模型更准确。
