Abstract
Medical image segmentation has become an essential technique in clinical and research-oriented applications. Because manual segmentation methods are tedious, and fully automatic segmentation lacks the flexibility of human intervention or correction, semi-automatic methods have become the preferred type of medical image segmentation. We present a hybrid, semi-automatic segmentation method in 3D that integrates both region-based and boundary-based procedures. Our method differs from previous hybrid methods in that we perform region-based and boundary-based approaches separately, which allows for more efficient segmentation. A region-based technique is used to generate an initial seed contour that roughly represents the boundary of a target brain structure, alleviating the local minima problem in the subsequent model deformation phase. The contour is deformed under a unique force equation independent of image edges. Experiments on MRI data show that this method can achieve high accuracy and efficiency primarily due to the unique seed initialization technique.
Abstract (translated)
医学图像分割已成为临床和科研应用中的一项重要技术。由于人工分割方法繁琐，全自动分割缺乏人工干预或校正的灵活性，半自动分割方法已成为医学图像分割的首选类型。我们提出了一种混合的，半自动的三维分割方法，结合了基于区域和基于边界的程序。我们的方法不同于以前的混合方法，我们分别执行基于区域和基于边界的方法，这使得分割更加有效。采用基于区域的技术生成一个初始种子轮廓，该轮廓大致代表目标大脑结构的边界，从而在随后的模型变形阶段缓解局部极小问题。轮廓在一个独立于图像边缘的独特力方程下变形。对磁共振成像数据的实验表明，该方法具有较高的精度和效率，主要是由于其独特的种子初始化技术。
URL
https://arxiv.org/abs/1904.09978
