Objectives

To develop and evaluate an automated method for prostate T2-weighted (T2W) image normalization using dual-reference (fat and muscle) tissue.

Materials and methods

Transverse T2W images from the publicly available PROMISE12 (N = 80) and PROSTATEx (N = 202) challenge datasets, and an in-house collected dataset (N = 60) were used. Aggregate channel features object detectors were trained to detect reference fat and muscle tissue regions, which were processed and utilized to normalize the 3D images by linear scaling. Mean prostate pseudo T2 values after normalization were compared to literature values. Inter-patient histogram intersections of voxel intensities in the prostate were compared between our approach, the original images, and other commonly used normalization methods. Healthy vs. malignant tissue classification performance was compared before and after normalization.

Results

The prostate pseudo T2 values of the three tested datasets (mean ± standard deviation = 78.49 ± 9.42, 79.69 ± 6.34 and 79.29 ± 6.30 ms) corresponded well to T2 values from literature (80 ± 34 ms). Our normalization approach resulted in significantly higher (p < 0.001) inter-patient histogram intersections (median = 0.746) than the original images (median = 0.417) and most other normalization methods. Healthy vs. malignant classification also improved significantly (p < 0.001) in peripheral (AUC 0.826 vs. 0.769) and transition (AUC 0.743 vs. 0.678) zones.

Conclusion

An automated dual-reference tissue normalization of T2W images could help improve the quantitative assessment of prostate cancer.

中文翻译：

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使用对象识别对前列腺的 T2 加权 MR 图像进行自动参考组织归一化。

目标

开发和评估使用双参考（脂肪和肌肉）组织进行前列腺 T2 加权 (T2W) 图像标准化的自动化方法。

材料和方法

从公众可获得的PROMISE12（横向T2W图像Ñ  = 80）和PROSTATEx（Ñ  = 202）挑战数据集，和一个内部数据集收集（Ñ 使用= 60）。聚合通道特征对象检测器被训练来检测参考脂肪和肌肉组织区域，这些区域被处理并用于通过线性缩放对 3D 图像进行归一化。将标准化后的平均前列腺假 T2 值与文献值进行比较。在我们的方法、原始图像和其他常用归一化方法之间比较了前列腺中体素强度的患者间直方图交叉点。比较正常化前后的健康与恶性组织分类性能。

结果

三个测试数据集的前列腺伪 T2 值（平均值 ± 标准偏差 = 78.49 ± 9.42、79.69 ± 6.34 和 79.29 ± 6.30 ms）与文献中的 T2 值（80 ± 34 ms）非常吻合。我们的归一化方法 比原始图像（中值 = 0.417）和大多数其他归一化方法显着更高（p < 0.001）患者间直方图交叉（中值 = 0.746）。 外周（AUC 0.826 对 0.769）和过渡（AUC 0.743 对 0.678）区域的健康与恶性分类也显着改善（p < 0.001）。

结论

T2W 图像的自动双参考组织标准化有助于改进前列腺癌的定量评估。