Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China.
Sci Rep. 2017 Jun 27;7(1):4274. doi: 10.1038/s41598-017-04276-6.
In this paper, we present an original multiple atlases level set framework (MALSF) for automatic, accurate and robust thalamus segmentation in magnetic resonance images (MRI). The contributions of the MALSF method are twofold. First, the main technical contribution is a novel label fusion strategy in the level set framework. Label fusion is achieved by seeking an optimal level set function that minimizes energy functional with three terms: label fusion term, image based term, and regularization term. This strategy integrates shape prior, image information and the regularity of the thalamus. Second, we use propagated labels from multiple registration methods with different parameters to take full advantage of the complementary information of different registration methods. Since different registration methods and different atlases can yield complementary information, multiple registration and multiple atlases can be incorporated into the level set framework to improve the segmentation performance. Experiments have shown that the MALSF method can improve the segmentation accuracy for the thalamus. Compared to ground truth segmentation, the mean Dice metrics of our method are 0.9239 and 0.9200 for left and right thalamus.
本文提出了一种新颖的多图谱水平集框架（MALSF），用于磁共振图像（MRI）中自动、准确和稳健的丘脑分割。MALSF 方法的贡献有两点。首先，主要的技术贡献是水平集框架中的一种新的标签融合策略。通过寻找最小化具有三个项的能量泛函的最优水平集函数来实现标签融合：标签融合项、基于图像的项和正则化项。该策略集成了形状先验、图像信息和丘脑的正则性。其次，我们使用具有不同参数的多种配准方法的传播标签，充分利用了不同配准方法的互补信息。由于不同的配准方法和不同的图谱可以产生互补的信息，因此可以将多种配准和多种图谱合并到水平集框架中，以提高分割性能。实验表明，MALSF 方法可以提高丘脑的分割准确性。与真实分割相比，我们的方法对于左右丘脑的平均 Dice 度量值分别为 0.9239 和 0.9200。
