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摘要:
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。
Abstract: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 determines 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.
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Overview: Computed tomography images have the advantage of fast imaging speed and sharp imaging. CT images are one of the most important medical imaging techniques for human evaluation and it has become a conventional means of daily inspection. For computer-aided diagnosis, interest towards segmentation of regions in CT images is an essential prerequisite. Therefore, it is imperative to seek an automatic CT image method that can replace manual segmentation. This paper presents a fully automated CT image segmentation method for human organs. Firstly, super-pixel meshing is performed on CT images based on the super-pixel segmentation, and super-pixel classification is performed by a convolutional neural network to determine edge super-pixels. Then, seed points of edge super-pixels are extracted to form initial contours. Finally, the initial contour is obtained based on the extraction by solving the minimum of the integrated energy function proposed herein. In order to comprehensively evaluate the segmentation effect of this method on medical CT images, this paper mainly divides CT image experiments into four organs, including the brain, liver, lungs, and vertebral body. The experimental results show that the super-pixel classification CNN has achieved excellent results in the super-pixel classification of CT images. The classification accuracy reaches 92%. The initial contour of the super-pixel seed points extracted in this paper is close to the organ edge, and the next contour based on a significant amount of time is stored in the solution of the integrated energy function. For the target image segmentation of brain, liver, lung, and vertebrae, the proposed method can accurately locate the edge super-pixels that completely extract the initial contour of the edge super-pixel seed point structure, and complete the segmentation contour subdivision by minimizing the improved integrated energy function. Compared with the advanced U-net method, the average Dice coefficient of the proposed method increase by 5%. It may provide a theoretical basis and a new solution for the diagnosis of clinical CT image lesions. In general, this approach can reduce time and improve efficiency while ensuring segmentation accuracy. In the future study, efforts would be made to test the framework on other types of medical images, such as MRI images and ultrasound images. At the same time, we also look forward to improving accuracy and efficiency and incorporating this framework into clinical diagnostics that benefit patients.
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图 2 不同参数K的超像素网格化结果。(a) K=500网格化结果；(b) K=2000网格化结果
Figure 2. Superpixel meshing results with different parameters of (a) K=500 and (b) K=2000
图 4 定量实验分割结果图(从上到下分别为脑部、肝脏、肺部及椎骨的分割结果)
Figure 4. Quantitative experiment segmentation results
图 6 超像素分类CNN边缘超像素分类结果
Figure 6. Super-pixel classification CNN edge classification results
表 1 超像素分类CNN网络结构参数
Table 1. The parameters of super-pixel classification CNN network
Layer Kernel Stride Pad Output Data - - - 64×64 Conv1 BN 2×2 - 1 0 64×64 - 0 64×64 Maxpool1 2×2 2 0 32×32 Conv2 2×2 1 0 32×32 Maxpool2 2×2 2 0 16×16 Conv3 2×2 1 0 16×16 Maxpool3 2×2 2 1 8×8 Conv4 2×2 1 0 8×8 Maxpool4 2×2 1 0 4×4 FC1 FC-4096 FC2 FC-1024 Soft-max Soft-max lables=1、0 
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表 2 数据表
Table 2. Data sheet
器官 数据集 脑 BrainWeb：脑数据库数据集，20组模型 椎骨 SpineWeb：腰椎分割CT图像数据库，3000张512×512切片 肝脏 Segmentation of the Liver：肝脏分割数据集，5500张512×512切片 肺 TIANCHI：开放的中文数据集，5000张512×512切片
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表 3 定量实验分割结果
Table 3. Quantitative experiment segmentation results
指标 任意初始轮廓 定位初始轮廓 本文方法 无边缘能量 有边缘能量 无边缘能量 有边缘能量 初始轮廓+边缘能量+后处理 Jaccard 0.545 0.773 0.881 0.943 0.945 Dice 0.548 0.778 0.892 0.947 0.948 CCR 0.547 0.775 0.890 0.944 0.945 分割耗时/(s/片) 45 43 23 27 25
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表 4 分割结果各评价指标
Table 4. Evaluation indicators of segmentation results
器官 超像素分类准确度 Jaccard Dice CCR 脑 0.928 0.955 0.955 0.954 椎骨 0.931 0.941 0.949 0.943 肝脏 0.940 0.943 0.976 0.977 0.976 肺 0.943 0.975 0.978 0.977
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