SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images

Aubreville M, Bertram CA, Klopfleisch R, Maier A (2018)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2018

Publisher: Springer Berlin Heidelberg

Edited Volumes: Informatik aktuell

Pages Range: 309-314

Conference Proceedings Title: Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen

Event location: Erlangen DE

URI: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2018/Aubreville18-S-A.pdf

DOI: 10.1007/978-3-662-56537-7_81

Abstract

Large-scale image data such as digital whole-slide histology images pose a challenging task at annotation software solutions. Today, a number of good solutions with varying scopes exist. For cell annotation, however, we find that many do not match the prerequisites for fast annotations. Especially in the field of mitosis detection, it is assumed that detection accuracy could significantly benefit from larger annotation databases that are currently however very troublesome to produce. Further, multiple independent (blind) expert labels are a big asset for such databases, yet there is currently no tool for this kind of annotation available. To ease this tedious process of expert annotation and grading, we introduce SlideRunner, an open source annotation and visualization tool for digital histopathology, developed in close cooperation with two pathologists. SlideRunner is capable of setting annotations like object centers (for e.g. cells) as well as object boundaries (e.g. for tumor outlines). It provides single-click annotations as well as a blind mode for multi-annotations, where the expert is directly shown the microscopy image containing the cells that he has not yet rated.

Authors with CRIS profile

Marc Aubreville Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Related research project(s)

Digital Pathology - New Approaches to the Automated Image Analysis of Histologic Slides Jan. 16, 2017 - Jan. 16, 2020

Involved external institutions

Freie Universität Berlin DE Germany (DE)

How to cite

APA:

Aubreville, M., Bertram, C.A., Klopfleisch, R., & Maier, A. (2018). SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images. In Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen (pp. 309-314). Erlangen, DE: Springer Berlin Heidelberg.

MLA:

Aubreville, Marc, et al. "SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images." Proceedings of the Bildverarbeitung für die Medizin 2018, Erlangen Springer Berlin Heidelberg, 2018. 309-314.

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