Background and objective

Accurate segmentation of prostate and its zones constitute an essential preprocessing step for computer-aided diagnosis and detection system for prostate cancer (PCa) using diffusion-weighted imaging (DWI). However, low signal-to-noise ratio and high variability of prostate anatomic structures are challenging for its segmentation using DWI. We propose a semi-automated framework that segments the prostate gland and its zones simultaneously using DWI.

Methods

In this paper, the Chan-Vese active contour model along with morphological opening operation was used for segmentation of prostate gland. Then segmentation of prostate zones into peripheral zone (PZ) and transition zone (TZ) was carried out using in-house developed probabilistic atlas with partial volume (PV) correction algorithm. The study cohort included MRI dataset of 18 patients (n = 18) as our dataset and methodology were also independently evaluated using 15 MRI scans (n = 15) of QIN-PROSTATE-Repeatability dataset. The atlas for zones of prostate gland was constructed using dataset of twelve patients of our patient cohort. Three-fold cross-validation was performed with 10 repetitions, thus total 30 instances of training and testing were performed on our dataset followed by independent testing on the QIN-PROSTATE-Repeatability dataset. Dice similarity coefficient (DSC), Jaccard coefficient (JC), and accuracy were used for quantitative assessment of the segmentation results with respect to boundaries delineated manually by an expert radiologist. A paired t-test was performed to evaluate the improvement in zonal segmentation performance with the proposed PV correction algorithm.

Results

For our dataset, the proposed segmentation methodology produced improved segmentation with DSC of 90.76 ± 3.68%, JC of 83.00 ± 5.78%, and accuracy of 99.42 ± 0.36% for the prostate gland, DSC of 77.73 ± 2.76%, JC of 64.46 ± 3.43%, and accuracy of 82.47 ± 2.22% for the PZ, and DSC of 86.05 ± 1.50%, JC of 75.80 ± 2.10%, and accuracy of 91.67 ± 1.56% for the TZ. The segmentation performance for QIN-PROSTATE-Repeatability dataset was, DSC of 85.50 ± 4.43%, JC of 75.00 ± 6.34%, and accuracy of 81.52 ± 5.55% for prostate gland, DSC of 74.40 ± 1.79%, JC of 59.53 ± 8.70%, and accuracy of 80.91 ± 5.16% for PZ, and DSC of 85.80 ± 5.55%, JC of 74.87 ± 7.90%, and accuracy of 90.59 ± 3.74% for TZ. With the implementation of the PV correction algorithm, statistically significant (p<0.05) improvements were observed in all the metrics (DSC, JC, and accuracy) for both prostate zones, PZ and TZ segmentation.

Conclusions

The proposed segmentation methodology is stable, accurate, and easy to implement for segmentation of prostate gland and its zones (PZ and TZ). The atlas-based segmentation framework with PV correction algorithm can be incorporated into a computer-aided diagnostic system for PCa localization and treatment planning.

中文翻译：

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使用基于概率图集的扩散加权MR图像对前列腺区域进行分割。

背景和目标

前列腺及其区域的准确分割是使用扩散加权成像（DWI）的计算机辅助诊断和检测前列腺癌（PCa）系统的必不可少的预处理步骤。但是，低信噪比和前列腺解剖结构的高可变性对于使用DWI进行分割具有挑战性。我们提出了一种半自动化的框架，该框架使用DWI同时分割前列腺及其区域。

方法

在本文中，Chan-Vese活动轮廓模型与形态学打开操作一起用于前列腺分割。然后使用内部开发的带有部分体积（PV）校正算法的概率图集将前列腺区域分为外周区域（PZ）和过渡区域（TZ）。该研究队列包括18位患者（n = 18）的MRI数据集，因为我们的数据集和方法学也使用QIN-PROSTATE-Repeatability数据集的15次MRI扫描（n = 15）进行了独立评估。使用我们这个患者队列的十二个患者的数据集构建了前列腺区域图集。通过10次重复执行三重交叉验证，因此对我们的数据集进行了总共30个训练和测试实例，然后对QIN-PROSTATE-Repeatability数据集进行了独立测试。骰子相似度系数（DSC），雅卡德系数（JC）和准确性用于对放射专家手工划定的边界的分割结果进行定量评估。进行了配对t检验，以评估所提出的PV校正算法对区域分割性能的改善。

结果

对于我们的数据集，提出的分割方法产生了更好的分割效果，DSC为90.76±3.68％，JC为83.00±5.78％，前列腺的准确性为99.42±0.36％，DSC为77.73±2.76％，JC为64.46±3.43 PZ的精度为82.47±2.22％，DSC的精度为86.05±1.50％，JC的精度为75.80±2.10％，TZ的精度为91.67±1.56％。QIN-PROSTATE-Repeatability数据集的分割性能为，DSC为85.50±4.43％，JC为75.00±6.34％，对前列腺的准确性为81.52±5.55％，DSC为74.40±1.79％，JC为59.53±8.70％ ，PZ的准确度为80.91±5.16％，DSC的准确度为85.80±5.55％，JC的准确度为74.87±7.90％，TZ的准确度为90.59±3.74％。随着PV校正算法的实施，在所有指标（DSC，JC，

结论

所提出的分割方法稳定，准确，并且易于实施，用于分割前列腺及其区域（PZ和TZ）。带有PV校正算法的基于图集的分割框架可以并入用于PCa定位和治疗计划的计算机辅助诊断系统。