School of Biomedical Engineering, Capital Medical University, Beijing, China.
Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.
Nat Commun. 2020 Nov 9;11(1):5669. doi: 10.1038/s41467-020-19392-7.
Medical image segmentation is crucial in diagnosing and treating diseases, but automatic segmentation of complex images is very challenging. Here we present a method, called the crack propagation method (CPM), based on the principles of fracture mechanics. This unique method converts the image segmentation problem into a mechanical one, extracting the boundary information of the target area by tracing the crack propagation on a thin plate with grooves corresponding to the area edge. The greatest advantage of CPM is in segmenting images involving blurred or even discontinuous boundaries, a task difficult to achieve by existing auto-segmentation methods. The segmentation results for synthesized images and real medical images show that CPM has high accuracy in segmenting complex boundaries. With increasing demand for medical imaging in clinical practice and research, this method will show its unique potential.
医学图像分割在疾病的诊断和治疗中至关重要，但对复杂图像的自动分割非常具有挑战性。在这里，我们提出了一种基于断裂力学原理的方法，称为裂缝扩展法（CPM）。这种独特的方法将图像分割问题转化为力学问题，通过在具有与目标区域边缘对应的凹槽的薄板上追踪裂缝的扩展，提取目标区域的边界信息。CPM 的最大优势在于分割涉及模糊甚至不连续边界的图像，这是现有自动分割方法难以实现的任务。对合成图像和真实医学图像的分割结果表明，CPM 在分割复杂边界方面具有很高的准确性。随着临床实践和研究中对医学成像需求的增加，这种方法将显示出其独特的潜力。
