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Automatic morphological sieving: comparison between different methods, application to DNA ploidy measurements.

C Boudry1, P Herlin, B Plancoulaine

  • 1Laboratoire d'Etudes et de Recherche sur les Matériaux, UPRESA CNRS 6004, Institut des Sciences de la Matière et du Rayonnement, Caen, France.

Analytical Cellular Pathology : the Journal of the European Society for Analytical Cellular Pathology
|December 28, 1999
PubMed
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This study introduces automatic methods to efficiently sort elements for DNA ploidy measurements, reducing time spent on manual sorting of nuclei, debris, and aggregates in archival tumors.

Area of Science:

  • Oncology
  • Biotechnology
  • Computational Biology

Background:

  • Accurate DNA ploidy measurements are crucial for cancer diagnosis and prognosis.
  • Manual sorting of cellular elements is time-consuming and subjective.
  • Archival tumor samples present unique challenges for analysis due to potential degradation and aggregation.

Purpose of the Study:

  • To develop and evaluate automatic methods for classifying and removing debris and aggregates from DNA ploidy measurements.
  • To compare the performance of mathematical morphology (MM), multiparametric analysis (MA), and neural network (NN) methods.
  • To assess the preservation of DNA ploidy abnormality information after automatic element sorting.

Main Methods:

  • Tested three automatic classification methods: mathematical morphology (MM), multiparametric analysis (MA), and neural network (NN).

Related Experiment Videos

  • Evaluated method performance against interactive sorting using archival brain tumor and breast carcinoma samples (7120 elements).
  • Quantified the percentage of debris and aggregates removed and the false positive rates for each method.

    • Main Results:

    • Automatic removal rates for debris and aggregates were 63% (MM), 75% (MA), and 85% (NN).
    • False positive rates were 6% (MM), 21% (MA), and 25% (NN).
    • DNA ploidy abnormality information was largely preserved using MM and MA methods, but not NN.

    Conclusions:

    • Automatic classification methods offer viable alternatives to tedious manual sorting for DNA ploidy measurements.
    • MM and MA methods effectively remove debris and aggregates while preserving critical diagnostic information.
    • NN method showed higher removal rates but compromised diagnostic accuracy in this context.