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Hyperparameter optimization for image analysis: application to prostate tissue images and live cell data of virus-infected cells

  • Christian RitterEmail author
  • Thomas Wollmann
  • Patrick Bernhard
  • Manuel Gunkel
  • Delia M. Braun
  • Ji-Young Lee
  • Jan Meiners
  • Ronald Simon
  • Guido Sauter
  • Holger Erfle
  • Karsten Rippe
  • Ralf Bartenschlager
  • Karl Rohr
Original Article

Abstract

Purpose

Automated analysis of microscopy image data typically requires complex pipelines that involve multiple methods for different image analysis tasks. To achieve best results of the analysis pipelines, method-dependent hyperparameters need to be optimized. However, complex pipelines often suffer from the fact that calculation of the gradient of the loss function is analytically or computationally infeasible. Therefore, first- or higher-order optimization methods cannot be applied.

Methods

We developed a new framework for zero-order black-box hyperparameter optimization called HyperHyper, which has a modular architecture that separates hyperparameter sampling and optimization. We also developed a visualization of the loss function based on infimum projection to obtain further insights into the optimization problem.

Results

We applied HyperHyper in three different experiments with different imaging modalities, and evaluated in total more than 400.000 hyperparameter combinations. HyperHyper was used for optimizing two pipelines for cell nuclei segmentation in prostate tissue microscopy images and two pipelines for detection of hepatitis C virus proteins in live cell microscopy data. We evaluated the impact of separating the sampling and optimization strategy using different optimizers and employed an infimum projection for visualizing the hyperparameter space.

Conclusions

The separation of sampling and optimization strategy of the proposed HyperHyper optimization framework improves the result of the investigated image analysis pipelines. Visualization of the loss function based on infimum projection enables gaining further insights on the optimization process.

Keywords

Microscopy image analysis Hyperparameter optimization Optimization framework Visualization 

Notes

Acknowledgements

This work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Projektnumber 240245660—SFB 1129 (projects P11, Z4) and the BMBF within the projects CancerTelSys (e:Med, #01ZX1602) and de.NBI (HD-HuB, #031A537C).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© CARS 2019

Authors and Affiliations

  • Christian Ritter
    • 1
    Email author
  • Thomas Wollmann
    • 1
  • Patrick Bernhard
    • 1
  • Manuel Gunkel
    • 2
  • Delia M. Braun
    • 3
  • Ji-Young Lee
    • 5
    • 6
  • Jan Meiners
    • 4
  • Ronald Simon
    • 4
  • Guido Sauter
    • 4
  • Holger Erfle
    • 2
  • Karsten Rippe
    • 3
  • Ralf Bartenschlager
    • 5
    • 6
  • Karl Rohr
    • 1
  1. 1.Biomedical Computer Vision GroupBioQuant, IPMB, University of Heidelberg and DKFZHeidelbergGermany
  2. 2.High-Content Analysis of the Cell (HiCell) and Advanced Biological Screening FacilityBioQuant, University of HeidelbergHeidelbergGermany
  3. 3.Division of Chromatin NetworksDKFZ and BioQuantHeidelbergGermany
  4. 4.Department of PathologyUniversity Medical Center Hamburg-EppendorfHamburgGermany
  5. 5.Department of Infectious Diseases, Molecular VirologyUniversity Hospital HeidelbergHeidelbergGermany
  6. 6.German Center of Infection Research, Heidelberg Partner SiteHeidelbergGermany

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