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A Combined Random Forests and Active Contour Model Approach for Fully Automatic Segmentation of the Left Atrium in Volumetric MRI.

Segmentation of the left atrium (LA) from cardiac magnetic resonance imaging (MRI) datasets is of great importance for image guided atrial fibrillation ablation, LA fibrosis quantification, and cardiac biophysical modelling. However, automated LA segmentation from cardiac MRI is challenging due to l...

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Published in:BioMed Research International Vol. 2017; pp. 1 - 15
Main Authors: Ma, Chao, Luo, Gongning, Wang, Kuanquan
Format: equations & formulas pictorial research tables/charts Journal Article
Published: Wiley-Blackwell 2/19/2017
Online Access:View this record in EBSCOhost
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      jtl: BioMed Research International
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      dt: 2/19/2017
      vid: 2017
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2017/8381094
        121332779
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        atl: A Combined Random Forests and Active Contour Model Approach for Fully Automatic Segmentation of the Left Atrium in Volumetric MRI.
      aug:
        au:
          Ma, Chao
          Luo, Gongning
          Wang, Kuanquan
        affil: Biocomputing Research Center, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
      sug:
        subj:
          Magnetic Resonance Imaging Methods
          Heart Atrium, Left Ultrasonography
          Imaging, Three-Dimensional
          Algorithms
          Conceptual Framework
          Decision Trees
          Automation
          Surgery, Computer-Assisted
          Catheter Ablation
          Atrial Fibrillation Surgery
          Validation Studies
      ab: Segmentation of the left atrium (LA) from cardiac magnetic resonance imaging (MRI) datasets is of great importance for image guided atrial fibrillation ablation, LA fibrosis quantification, and cardiac biophysical modelling. However, automated LA segmentation from cardiac MRI is challenging due to limited image resolution, considerable variability in anatomical structures across subjects, and dynamic motion of the heart. In this work, we propose a combined random forests (RFs) and active contour model (ACM) approach for fully automatic segmentation of the LA from cardiac volumetric MRI. Specifically, we employ the RFs within an autocontext scheme to effectively integrate contextual and appearance information from multisource images together for LA shape inferring. The inferred shape is then incorporated into a volume-scalable ACM for further improving the segmentation accuracy. We validated the proposed method on the cardiac volumetric MRI datasets from the STACOM 2013 and HVSMR 2016 databases and showed that it outperforms other latest automated LA segmentation methods. Validation metrics, average Dice coefficient (DC) and average surface-to-surface distance (S2S), were computed as 0.9227±0.0598 and 1.14±1.205 mm, versus those of 0.6222–0.878 and 1.34–8.72 mm, obtained by other methods, respectively.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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