title: Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis.
abstract: BACKGROUND
  Research and diagnosis in medicine and biology often require the assessment of a large amount of microscopy image data. Although on the one hand, digital pathology and new bioimaging technologies find their way into clinical practice and pharmaceutical research, some general methodological issues in automated image analysis are still open.
  
  
  METHODS
  In this study, we address the problem of fitting the parameters in a microscopy image segmentation pipeline. We propose to fit the parameters of the pipeline's modules with optimization algorithms, such as, genetic algorithms or coordinate descents, and show how visual exploration of the parameter space can help to identify sub-optimal parameter settings that need to be avoided.
  
  
  RESULTS
  This is of significant help in the design of our automatic parameter fitting framework, which enables us to tune the pipeline for large sets of micrographs.
  
  
  CONCLUSIONS
  The underlying parameter spaces pose a challenge for manual as well as automated parameter optimization, as the parameter spaces can show several local performance maxima. Hence, optimization strategies that are not able to jump out of local performance maxima, like the hill climbing algorithm, often result in a local maximum.
authors.affiliation: Department for Image Processing and Biomedical Engineering, Fraunhofer Institute for Integrated Circuits, Erlangen, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Held
authors.forename: Tim
authors.initials: T
authors.lastname: Nattkemper
authors.forename: Ralf
authors.initials: R
authors.lastname: Palmisano
authors.forename: Thomas
authors.initials: T
authors.lastname: Wittenberg
keywords: Fluorescence; image processing; microscopy; parameter optimization; segmentation