BACKGROUND Segmentation of prostate from magnetic resonance images (MRI) is a critical process for guiding prostate puncture and biopsy. Currently, the best results are obtained by Convolutional Neural Network (CNN). However, challenges still exist when applying CNN to segment prostate, such as data distribution issue caused by insubstantial and inconsistent intensity levels and vague boundaries in MRI. OBJECTIVE To segment prostate gland from a MRI dataset including different prostate images with limited resolution and quality. METHODS We propose and apply a global histogram matching approach to make intensity distribution of the MRI dataset closer to uniformity. To capture the real boundaries and improve segmentation accuracy, we employ a module of variational models to help improve performance. RESULTS Using seven evaluation metrics to quantify improvements of our proposed fusion approach compared with the state of art V-net model resulted in increase in the Dice Coefficient (11.2%), Jaccard Coefficient (13.7%), Volumetric Similarity (12.3%), Adjusted Rand Index (11.1%), Area under ROC Curve (11.6%), and reduction of the Mean Hausdorff Distance (16.1%) and Mahalanobis Distance (2.8%). The 3D reconstruction also validates the advantages of our proposed framework, especially in terms of smoothness, uniformity, and accuracy. In addition, observations from the selected examples of 2D visualization show that our segmentation results are closer to the real boundaries of the prostate, and better represent the prostate shapes. CONCLUSIONS Our proposed approach achieves significant performance improvements compared with the existing methods based on the original CNN or pure variational models.

中文翻译：

---

基于深度网络和变异方法之间融合的自动前列腺分割。

背景技术从磁共振图像（MRI）分割前列腺是引导前列腺穿刺和活检的关键过程。目前，最好的结果是通过卷积神经网络（CNN）获得的。然而，当将CNN应用于前列腺分割时，仍然存在挑战，例如强度水平不一致和不一致以及MRI中边界模糊所引起的数据分布问题。目的从MRI数据集中分割前列腺，包括分辨率和质量有限的不同前列腺图像。方法我们提出并应用全局直方图匹配方法，以使MRI数据集的强度分布更接近均匀性。为了捕获实际边界并提高细分精度，我们采用了变分模型模块来帮助提高性能。结果与现有的V-net模型相比，使用七个评估指标来量化我们提出的融合方法的改进导致骰子系数（11.2％），雅卡德系数（13.7％），体积相似度（12.3％），已调整兰德指数（11.1％），ROC曲线下面积（11.6％）以及平均Hausdorff距离（16.1％）和马氏距离（2.8％）的降低。3D重建还验证了我们提出的框架的优势，特别是在平滑性，均匀性和准确性方面。此外，从2D可视化的选定示例中观察到的结果表明，我们的分割结果更接近前列腺的真实边界，并且更好地表示了前列腺的形状。