1Department of Radiology, Seoul National University Hospital, Seoul, Korea.
Ultrason Imaging. 2013 Oct;35(4):333-43. doi: 10.1177/0161734613505998.
Rapid volume density analysis (RVDA) for automated breast ultrasound (ABUS) has been proposed as a more efficient method for estimating breast density. In the current experiment, ABUS images were obtained for 67 breasts from 40 patients. For each case, three rectangular volumes of interest (VOIs) were extracted, including the VOIs located at the 6 and 12 o'clock positions relative to the nipple in the anterior to posterior pass and the lateral position relative to the nipple in the lateral pass. The centers of these VOIs were defined to align with the center of nipple, and the depths reached the retromammary fat boundary. The fuzzy c-means classifier was applied to differentiate the fibroglandular and fat tissues to estimate the density. The classification results of the three VOIs were averaged to obtain the breast density. The density correlations between the RVDA and the ABUS methods were 0.98 and 0.96 using Pearson's correlation and linear regression coefficients, respectively. The average computation times for RVDA and ABUS were 4.2 and 17.8 seconds, respectively, using an Intel Core2 2.66 GHz computer with 3.25 GB memory. In conclusion, the RVDA method offers a quantitative and efficient breast density estimation for ABUS.
快速容积密度分析(RVDA)已被提议作为一种更有效的方法来估计乳腺密度,用于自动乳腺超声(ABUS)。在目前的实验中,从 40 名患者中获得了 67 个乳房的 ABUS 图像。对于每个病例,提取了三个感兴趣的矩形体积(VOI),包括相对于乳头在前后通过中 6 点和 12 点位置的 VOI,以及相对于乳头在外侧通过中的外侧位置的 VOI。这些 VOI 的中心被定义为与乳头中心对齐,并达到乳腺后脂肪边界的深度。应用模糊 C 均值分类器来区分纤维腺体和脂肪组织以估计密度。三个 VOI 的分类结果进行平均以获得乳房密度。使用 Pearson 相关系数和线性回归系数,RVDA 和 ABUS 方法之间的密度相关性分别为 0.98 和 0.96。使用具有 3.25GB 内存的 Intel Core2 2.66GHz 计算机,RVDA 和 ABUS 的平均计算时间分别为 4.2 和 17.8 秒。总之,RVDA 方法为 ABUS 提供了一种定量和有效的乳腺密度估计方法。