A computerized volumetric segmentation method applicable to multi-centre MRI data to support computer-aided breast tissue analysis, density assessment and lesion localization.
A computerized volumetric segmentation method applicable to multi-centre MRI data to support computer-aided breast tissue analysis, density assessment and lesion localization.
复制标题
一种适用于多中心MRI数据的计算机容量分割方法，用于支持计算机辅助乳腺组织分析，密度评估和病变定位。
DOI：
10.1007/s11517-016-1484-y
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发表时间：
2017-01
影响因子：
3.2
通讯作者：
Leach MO
中科院分区：
文献类型：
--
作者：
Ertas G;Doran SJ;Leach MO
乳腺MRI中的密度评估和病变定位需要准确分割乳腺组织。一个快速的，计算机化的算法体积乳腺分割，适用于多中心数据，已开发，采用三维偏差校正模糊c-均值聚类和形态学操作。在T1加权图像上确定完整的乳房范围，而无需有关乳房解剖结构的先验信息。使用胸骨中部的自动检测分别识别左乳房和右乳房。对在英国MRI筛查的多中心研究中扫描的82名女性的乳房体积进行的统计分析表明，与手动校正的分割相比，分割算法表现良好，具有高相对重叠（RO）、高真阳性体积分数（TPVF）和低假阳性体积分数（FPVF），并且具有RO 0.94 ± 0.05的总体性能，TPVF 0.97 ± 0.03和FPVF 0.04 ± 0.06（训练：0.93 ± 0.05、0.97 ± 0.03和0.04 ± 0.06;测试：0.94 ± 0.05、0.98 ± 0.02和0.05 ± 0.07）。
Density assessment and lesion localization in breast MRI require accurate segmentation of breast tissues. A fast, computerized algorithm for volumetric breast segmentation, suitable for multi-centre data, has been developed, employing 3D bias-corrected fuzzy c-means clustering and morphological operations. The full breast extent is determined on T1-weighted images without prior information concerning breast anatomy. Left and right breasts are identified separately using automatic detection of the midsternum. Statistical analysis of breast volumes from eighty-two women scanned in a UK multi-centre study of MRI screening shows that the segmentation algorithm performs well when compared with manually corrected segmentation, with high relative overlap (RO), high true-positive volume fraction (TPVF) and low false-positive volume fraction (FPVF), and has an overall performance of RO 0.94 ± 0.05, TPVF 0.97 ± 0.03 and FPVF 0.04 ± 0.06, respectively (training: 0.93 ± 0.05, 0.97 ± 0.03 and 0.04 ± 0.06; test: 0.94 ± 0.05, 0.98 ± 0.02 and 0.05 ± 0.07).