DeconvTest: Simulation framework for quantifying errors and selecting optimal parameters of image deconvolution. Issue 4 (3rd February 2020)

• Record Type: Journal Article
• Authors: Medyukhina, Anna; Figge, Marc Thilo
• Is Part Of: Journal of biophotonics. Volume 13:Issue 4(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-03
• Subjects: deconvolution -- open‐source software -- performance evaluation; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.201960079
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: Deconvolution is an essential step of image processing that aims to compensate for the image blur caused by the microscope's point spread function. With many existing deconvolution methods, it is challenging to choose the method and its parameters most appropriate for particular image data at hand. To facilitate this task, we developed DeconvTest: an open‐source Python‐based framework for generating synthetic microscopy images, deconvolving them with different algorithms, and quantifying reconstruction errors. In contrast to existing software, DeconvTest combines all components required to analyze deconvolution performance in a systematic, high‐throughput and quantitative manner. We demonstrate the power of the framework by using it to identify the optimal deconvolution settings for synthetic and real image data. Based on this, we provide a guideline for (a) choosing optimal values of deconvolution parameters for image data at hand and (b) optimizing imaging conditions for best results in combination with subsequent image deconvolution. Abstract : DeconvTest is a Python‐based simulation framework that allows the user to quantify and compare the performance of different deconvolution methods. The framework integrates all components needed for such quantitative comparison: it (a) generates synthetic ground truth images through in silico microscopy experiments, (b) deconvolves the images with multiple algorithms and (c) quantifies the deconvolution accuracy by various performance measures.