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Algorithm versus physicians variability evaluation in the cardiac chambers extraction.

Abstract

Congenital heart diseases are present in eight of every 1000 newborns. The diagnosis of those pathologies usually depends on the available imaging methods. A correct diagnosis requires a detailed observation of the heart chambers, wall motions, valves function, and quantitative evaluation of the cavity volumes. For that goal numerous automatic algorithms have been proposed to segment the echocardiographic images. In this paper, the authors evaluate the performance of a level set algorithm based on the phase symmetry approach and on a new logarithmic-based stopping function to extract the heart cavity contours simultaneously, and in a fully automatic way. The extracted cardiac borders are then statistically compared with the ones manually sketched by four physicians on a set of 240 cavities. Nonparametric statistical tests are conducted on the data using several figures of merit, in order to study the inter- and intraobserver variabilities among the four physicians and the level set algorithm, concerning to the extracted contours. The results show there is a great concordance about all the used similarity indexes. A higher interobserver variability was found among the physicians than the variability obtained when the algorithm versus physician performance is compared. The statistical analysis suggests the proposed algorithm produces results similar to the ones provided by the physicians.

Links

  • Publisher Full Text
  • Authors

    Silva JS, Santos JB, Roxo D, Martins P, Castela E, Martins R

    Source

    IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society 16:5 2012 Sep pg 835-41

    MeSH

    Algorithms
    Child
    Databases, Factual
    Echocardiography
    Heart Atria
    Heart Ventricles
    Humans
    Image Processing, Computer-Assisted
    Observer Variation
    Physicians

    Pub Type(s)

    Journal Article

    Language

    eng

    PubMed ID

    22736653