Guided interactive image segmentation using machine learning and color-based image set clustering.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Over the last decades, image processing and analysis have become one of the key technologies in systems biology and medicine. The quantification of anatomical structures and dynamic processes in living systems is essential for understanding the complex underlying mechanisms and allows, i.e. the construction of spatio-temporal models that illuminate the interplay between architecture and function. Recently, deep learning significantly improved the performance of traditional image analysis in cases where imaging techniques provide large amounts of data. However, if only a few images are available or qualified annotations are expensive to produce, the applicability of deep learning is still limited.

Authors

  • Adrian Friebel
    Institute of Computer Science, Leipzig University, Leipzig 04107, Germany.
  • Tim Johann
    IfADo-Leibniz Research Centre for Working Environment and Human Factors, Dortmund 44139, Germany.
  • Dirk Drasdo
    IfADo-Leibniz Research Centre for Working Environment and Human Factors, Dortmund 44139, Germany.
  • Stefan Hoehme
    Institute of Computer Science, Leipzig University, Leipzig 04107, Germany.