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64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

08. - 11.09.2019, Dortmund

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AutoLane: An Open-source Tool for Semi-automatic Analysis of Gel Electrophoresis Images

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  • Mark Weber - Uniklinik RWTH Aachen, Aachen, Germany
  • Viorel-Marin Rusu - R&D Department, Magtovio b.v, Aachen, Germany
  • Elisa A. Liehn - Uniklinik RWTH Aachen, Aachen, Germany
  • Roberta Florescu - Uniklinik RWTH Aachen, Aachen, Germany
  • Alexander Schuh - Uniklinik RWTH Aachen, Aachen, Germany
  • Mihaela Rusu - Uniklinik RWTH Aachen, Aachen, Germany
  • Stephan Jonas - Technical University of Munich, München, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Dortmund, 08.-11.09.2019. Düsseldorf: German Medical Science GMS Publishing House; 2019. DocAbstr. 70

doi: 10.3205/19gmds098, urn:nbn:de:0183-19gmds0989

Published: September 6, 2019

© 2019 Weber et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.

Outline

Text

Analyzing gel electrophoresis images of protein fractions with low vs. high molecular weight is an important task for the discovery of cardiac biomarkers in the context of myocardial infarction. Manual thresholding in image processing presents a bottleneck for rapid image analysis while leading to reproducibility issues. In the attempt to overcome these issues, we propose a novel semi-automatic analysis system that offers a robust lane detection and information extraction algorithm. The proposed algorithm is based on four main steps: (1) pre-processing, (2) noise-filtering, (3) gradient-based detection and (4) molecular weight extraction. Our detection algorithm uses a custom variant of the Canny edge detector and clustering, while the extraction procedure uses curve fitting to determine molecular weights based on the reference ladders. Our experimental results show that the proposed method eliminates image artefacts while preserving the image characteristics and improves the overall image analysis. Moreover, we open-source our tool to enable researchers to reliably, reproducibly and robustly produce results from gel electrophoresis images.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.

Outline

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