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A Multiorgan Segmentation Model for CT Volumes via Full Convolution-Deconvolution Network.

We propose a model with two-stage process for abdominal segmentation on CT volumes. First, in order to capture the details of organs, a full convolution-deconvolution network (FCN-DecNet) is constructed with multiple new unpooling, deconvolutional, and fusion layers. Then, we optimize the coarse seg...

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Published in:BioMed Research International Vol. 2017; pp. 1 - 10
Main Authors: Yang, Yangzi, Jiang, Huiyan, Sun, Qingjiao
Format: diagnostic images equations & formulas research tables/charts Journal Article
Published: Wiley-Blackwell 9/17/2017
Online Access:View this record in EBSCOhost
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      dt: 9/17/2017
      vid: 2017
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2017/6941306
        125187544
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        atl: A Multiorgan Segmentation Model for CT Volumes via Full Convolution-Deconvolution Network.
      aug:
        au:
          Yang, Yangzi
          Jiang, Huiyan
          Sun, Qingjiao
        affil: Software College, Northeastern University, Shenyang 110819, China
      sug:
        subj:
          Abdomen Anatomy and Histology
          Tomography, X-Ray Computed
          Algorithms
          Signal Processing, Computer Assisted
          Diagnostic Imaging
          Mathematics
          Data Analysis Software
          Descriptive Statistics
          Models, Biological
          Funding Source
      ab: We propose a model with two-stage process for abdominal segmentation on CT volumes. First, in order to capture the details of organs, a full convolution-deconvolution network (FCN-DecNet) is constructed with multiple new unpooling, deconvolutional, and fusion layers. Then, we optimize the coarse segmentation results of FCN-DecNet by multiscale weights probabilistic atlas (MS-PA), which uses spatial and intensity characteristic of atlases. Our coarse-fine model takes advantage of intersubject variability, spatial location, and gray information of CT volumes to minimize the error of segmentation. Finally, using our model, we extract liver, spleen, and kidney with Dice index of 90.1 ± 1%, 89.0 ± 1.6%, and 89.0 ± 1.3%, respectively.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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