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

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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.

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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).

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Authors and Affiliations

  1. Biomedical Computer Vision Group, BioQuant, IPMB, University of Heidelberg and DKFZ, Im Neuenheimer Feld 267, Heidelberg, Germany

    Christian Ritter, Thomas Wollmann, Patrick Bernhard & Karl Rohr

  2. High-Content Analysis of the Cell (HiCell) and Advanced Biological Screening Facility, BioQuant, University of Heidelberg, Im Neuenheimer Feld 267, Heidelberg, Germany

    Manuel Gunkel & Holger Erfle

  3. Division of Chromatin Networks, DKFZ and BioQuant, Im Neuenheimer Feld 267, Heidelberg, Germany

    Delia M. Braun & Karsten Rippe

  4. Department of Pathology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg, Germany

    Jan Meiners, Ronald Simon & Guido Sauter

  5. Department of Infectious Diseases, Molecular Virology, University Hospital Heidelberg, Im Neuenheimer Feld 344, Heidelberg, Germany

    Ji-Young Lee & Ralf Bartenschlager

  6. German Center of Infection Research, Heidelberg Partner Site, Heidelberg, Germany

    Ji-Young Lee & Ralf Bartenschlager

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  1. Christian Ritter
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  2. Thomas Wollmann
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  3. Patrick Bernhard
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  9. Guido Sauter
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  10. Holger Erfle
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Correspondence to Christian Ritter.

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Ritter, C., Wollmann, T., Bernhard, P. et al. Hyperparameter optimization for image analysis: application to prostate tissue images and live cell data of virus-infected cells. Int J CARS 14, 1847–1857 (2019). https://doi.org/10.1007/s11548-019-02010-3

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  • DOI: https://doi.org/10.1007/s11548-019-02010-3

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