Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks.

Journal: Journal of digital imaging
Published Date: Oct 1, 2018

Abstract

Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ segmentation framework is presented. The framework utilized convolution neural networks (CNN) to classify pixels. To reduce the redundant inputs, the simple linear iterative clustering (SLIC) of super-pixels and the support vector machine (SVM) classifier are introduced. To establish the perfect boundary of organs in one-pixel-level, the pixels need to be classified step-by-step. First, the SLIC is used to cut an image into grids and extract respective digital signatures. Next, the signature is classified by the SVM, and the rough edges are acquired. Finally, a precise boundary is obtained by the CNN, which is based on patches around each pixel-point. The framework is applied to abdominal CT scans of livers and high-resolution computed tomography (HRCT) scans of lungs. The experimental CT scans are derived from two public datasets (Sliver 07 and a Chinese local dataset). Experimental results show that the proposed method can precisely and efficiently detect the organs. This method consumes 38 s/slice for liver segmentation. The Dice coefficient of the liver segmentation results reaches to 97.43%. For lung segmentation, the Dice coefficient is 97.93%. This finding demonstrates that the proposed framework is a favorable method for lung segmentation of HRCT scans.

Authors

  • Xiaoming Liu
    College of Agriculture, Northeast Agricultural University, Harbin, China.
  • Shuxu Guo
    College of Electronic Science and Engineering, Jilin University, Changchun, China.
  • Bingtao Yang
    College of Communication Engineering, Jilin University, Changchun, 130012, China.
  • Shuzhi Ma
    College of Electronic Science and Engineering, Jilin University, Changchun, China.
  • Huimao Zhang
    Department of Radiology, The First Hospital of Jilin University, No.1, Xinmin Street, Changchun 130021, China (Y.W., M.L., Z.M., J.W., K.H., Q.Y., L.Z., L.M., H.Z.). Electronic address: huimao@jlu.edu.cn.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Changjian Sun
    College of Electronic Science and Engineering, Jilin University, Changchun, China.
  • Lanyi Jin
    College of Electronic Science and Engineering, Jilin University, Changchun, China.
  • Xueyan Li
    College of Electronic Science and Engineering, Jilin University, Changchun, China. Electronic address: leexy@jlu.edu.cn.
  • Qi Yang
    Department of Radiology, The First Hospital of Jilin University, No.1, Xinmin Street, Changchun 130021, China (Y.W., M.L., Z.M., J.W., K.H., Q.Y., L.Z., L.M., H.Z.).
  • Yu Fu
    Molecular Diagnosis and Treatment Center for Infectious Diseases Dermatology Hospital Southern Medical University Guangzhou China.

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