Liu X, Gan Q, Liu X, et al. Joint energy active contour CT image segmentation method based on super-pixel[J]. Opto-Electron Eng, 2020, 47(1): 190104. doi: 10.12086/oee.2020.190104
Citation: Liu X, Gan Q, Liu X, et al. Joint energy active contour CT image segmentation method based on super-pixel[J]. Opto-Electron Eng, 2020, 47(1): 190104. doi: 10.12086/oee.2020.190104

Joint energy active contour CT image segmentation method based on super-pixel

    Fund Project: Supported by National Natural Science Foundation of China (61172167) and Heilongjiang Natural Science Foundation (QC2017076)
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  • Corresponding author: Wang Bo, E-mail: hust_wb@126.com
  • 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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    沈阳化工大学材料科学与工程学院 沈阳 110142

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