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.
Joint energy active contour CT image segmentation method based on super-pixel
First published at:Jan 14, 2020
 Moltz J H, Bornemann L, Dicken V, et al. Segmentation of liver metastases in CT scans by adaptive thresholding and morphological processing[C]//The MIDAS Journal-Grand Challenge Liver Tumor Segmentation (2008 MICCAI Workshop), 2008, 472: 195–222.
 Chang Y L, Li X B. Adaptive image region-growing[J]. IEEE Transactions on Image Processing, 1994, 3(6): 868–872.
 Pohle R, Toennies K D. Segmentation of medical images using adaptive region growing[J]. Proceedings of SPIE, 2001, 4322: 1337–1346.
 Oda M, Nakaoka T, Kitasaka T, et al. Organ segmentation from 3D abdominal CT images based on atlas selection and graph cut[C]//Proceedings of the Third International Conference on Abdominal Imaging: Computational and Clinical Applications, 2012, 7029: 181–188.
 Criminisi A, Shotton J, Robertson D, et al. Regression forests for efficient anatomy detection and localization in CT stud-ies[C]//International MICCAI Workshop, MCV 2010, 2011: 106–117.
 Tang L M, Tian X Q, Huang D R, et al. Image segmentation model combined with FCMS and variational level set[J]. Acta Automatica Sinica, 2014, 40(6): 1233–1248.
唐利明, 田学全, 黄大荣, 等. 结合FCMS与变分水平集的图像分割模型[J]. 自动化学报, 2014, 40(6): 1233–1248.
 Chen K, Li B, Tian L F. A segmentation algorithm of pulmonary nodules using active contour model based on fuzzy speed function[J]. Acta Automatica Sinica, 2013, 39(8): 1257–1264.
陈侃, 李彬, 田联房. 基于模糊速度函数的活动轮廓模型的肺结节分割[J]. 自动化学报, 2013, 39(8): 1257–1264.
 Sun W Y, Dong E Q, Cao Z L, et al. A robust local segmentation method based on fuzzy-energy based active contour[J]. Acta Automatica Sinica, 2017, 43(4): 611–621.
孙文燕, 董恩清, 曹祝楼, 等. 一种基于模糊主动轮廓的鲁棒局部分割方法[J]. 自动化学报, 2017, 43(4): 611–621.
 Jones J L, Xie X H, Essa E. Combining region-based and imprecise boundary-based cues for interactive medical image segmentation[J]. International Journal for Numerical Methods in Biomedical Engineering, 2014, 30(12): 1649–1666.
 Tah A A, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool[J]. BMC Medical Imaging, 2015, 15(1): 29.
 Criminisi A, Robertson D, Konukoglu E, et al. Regression forests for efficient anatomy detection and localization in computed tomography scans[J]. Medical Image Analysis, 2013, 17(8): 1293–1303.
 Shin H C, Orton M R, Collins D J, et al. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1930–1943.
 Wang Z, Yang J. Automated detection of diabetic retinopathy using deep convolutional neural networks[J]. Medical Physics, 2016, 43(6): 3406.
 Kooi T, Litjens G, Van Ginneken B, et al. Large scale deep learning for computer aided detection of mammographic le-sions[J]. Medical Image Analysis, 2017, 35: 303–312.
 Tao Y P, Jing Y, Xu C. CT image segmentation method combining superpixel and CNN[J]. Computer Engineering and Applications, 2019: 1–8.
陶永鹏, 景雨, 顼聪. 融合超像素和CNN的CT图像分割方法[J]. 计算机工程与应用, 2019: 1–8.
 Yu L Q, Yang X, Hao C, et al. Volumetric ConvNets with mixed residual connections for automated prostate segmentation from 3D MR images[C]//Proceedings of the 31th AAAI Conference on Artificial Intelligence (AAAI-17), 2017: 66–72.
 Sevastopolsky A. Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network[J]. Pattern Recognition and Image Analysis, 2017, 27(3): 618–624.
 Milletari F, Navab N, Ahmadi S A. V-Net: fully convolutional neural networks for volumetric medical image segmenta-tion[C]//2016 Fourth International Conference on 3D Vision (3DV), 2016: 565–571.
 Ren M. Learning a classification model for segmenta-tion[C]//Proceedings Ninth IEEE International Conference on Computer Vision, 2003: 10–17.
 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[Z]. arXiv:1409.1556, 2015.
 Aghaei F, Ross S R, Wang Y Z, et al. Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images[C]//Proceedings Volume 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 2017: 10138.
 Korez R, Ibragimov B, Likar B, et al. Interpolation-based shape-constrained deformable model approach for segmentation of vertebrae from CT spine images[C]//Recent Advances in Computational Methods and Clinical Applications for Spine Imaging, 2015: 235–240.
 Liu X M, Guo S X, Yang B T, et al. Automatic organ segmentation for CT scans based on super-pixel and convolutional neural networks[J]. Journal of Digital Imaging, 2018, 31(5): 748–760.
National Natural Science Foundation of China (61172167) and Heilongjiang Natural Science Foundation (QC2017076)
Get Citation: Liu Xia, Gan Quan, Liu Xiao, et al. Joint energy active contour CT image segmentation method based on super-pixel[J]. Opto-Electronic Engineering, 2020, 47(1): 190104.