基于模板的自动 MRI 乳房分割，排除胸部区域。
Template-based automatic breast segmentation on MRI by excluding the chest region.
机构信息
Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697-5020 and National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Department of Biomedical Engineering, School of Medicine, Shenzhen University, 518060 China.
出版信息
Med Phys. 2013 Dec;40(12):122301. doi: 10.1118/1.4828837.
PURPOSE
Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method.
METHODS
Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary of the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subject's image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis.
RESULTS
The breast volumes measured by the proposed algorithm were very close to the radiologist's corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1.03%. The total error by adding the inclusion and exclusion errors ranged from 0.16% to 11.8%, with a mean of 2.89% ± 2.55%.
CONCLUSIONS
The automatic chest template-based breast MRI segmentation method worked well for cases with different body and breast shapes and different density patterns. Compared to the radiologist-established truth, the mean difference in segmented breast volume was approximately 1%, and the total error by considering the additive inclusion and exclusion errors was approximately 3%. This method may provide a reliable tool for MRI-based segmentation of breast density.
目的
目前，使用半自动方法对 MRI 中的乳房密度进行定量分析的方法较为常用。在本研究中，作者报告了一种完全基于自动胸部模板的方法。
方法
对 31 名健康女性的非脂肪抑制乳腺 MRI 图像进行了分析。其中，随机选择一个病例作为模板，其余 30 个病例用于测试。与大多数基于模型的乳腺分割方法不同，这些方法使用乳腺区域作为模板，而本研究则使用中层面的胸部区域作为模板。在胸部模板内，确定三个身体标志（胸椎和双侧胸大肌边界），以执行初始 V 形切割，从而确定乳腺的后外侧边界。将胸部模板映射到每个受试者的图像空间，以获得用于排除的受试者特定的胸部模型。在剩余的图像上，识别并排除胸壁肌肉以获得干净的乳腺分割。在中层面上确定的胸壁和肌肉边界用作分割相邻层面的参考，然后继续向上和向下，直到分割完所有 3D 层面。由一位有经验的放射科医生评估分割结果，以标记出因错误包含或排除而错误的体素，进行误差分析。
结果
该算法测量的乳房体积与放射科医生校正的体积非常接近，在 30 个经过测试的受试者中，%差异范围为 0.01%至 3.04%，平均值为 0.86%±0.72%。总误差通过添加包含误差和排除误差（因此它们不会相互抵消）来计算，在 30 个经过测试的受试者中，总误差范围为 0.05%至 6.75%，平均值为 3.05%±1.93%。算法和放射科医生确定的乳腺内纤维腺体组织的分割也非常接近，%差异范围为 0.02%至 2.52%，平均值为 1.03%±1.03%。通过添加包含误差和排除误差的总误差范围为 0.16%至 11.8%，平均值为 2.89%±2.55%。
结论
基于自动胸部模板的乳腺 MRI 分割方法对具有不同体型和乳房形状以及不同密度模式的病例均适用。与放射科医生确定的“真实”结果相比，分割乳房体积的平均差异约为 1%，考虑到包含和排除误差的总误差约为 3%。该方法可为基于 MRI 的乳腺密度分割提供一种可靠的工具。
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