为解决医学 CT图像主动轮廓分割方法中对初始轮廓敏感的问题, 提出一种基于超像素和卷积神经网络的人体器官 CT图像联合能量函数主动轮廓分割方法。该方法首先基于超像素分割对 CT图像进行超像素网格化, 并通过卷积神经网络进行超像素分类确定边缘超像素; 然后提取边缘超像素的种子点组成初始轮廓; 最后在提取的初始轮廓基础上, 通过求解本文提出的综合能量函数最小值实现人体器官分割。实验结果表明, 本文方法与先进的 U-Net方法相比平均 Dice系数提高 5%, 为临床 CT图像病变诊断提供理论基础和新的解决方案。

In this paper, an active contour segmentation method for organs CT images based on super-pixel and convolutional neural network is proposed to solve the sensitive problem of the initial contour of the segmentation method of the CT image. The method firstly super-pixels the CT image based on super-pixel segmentation and de-termines the edge super-pixels by the super-pixel classification through a convolutional neural network. Afterwards, the seed points of the edge super-pixels are extracted to form the initial contour. Finally, based on the extracted initial contour, the human organ segmentation is realized by solving the minimum value of the integrated energy function proposed in this paper. The results in this paper show that the average Dice coefficient is improved by 5% compared with the advanced U-Net method, providing a theoretical basis and a new solution for the diagnosis of clinical CT image lesions.