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- Title
TissueWand, a Rapid Histopathology Annotation Tool.
- Authors
Lindvall, Martin; Sanner, Alexander; Petré, Fredrik; Lindman, Karin; Treanor, Darren; Lundström, Claes; Löwgren, Jonas
- Abstract
Background: Recent advancements in machine learning (ML) bring great possibilities for the development of tools to assist with diagnostic tasks within histopathology. However, these approaches typically require a large amount of ground truth training data in the form of image annotations made by human experts. As such annotation work is a very time-consuming task, there is a great need for tools that can assist in this process, saving time while not sacrificing annotation quality. Methods: In an iterative design process, we developed TissueWand - an interactive tool designed for efficient annotation of gigapixel-sized histopathological images, not being constrained to a predefined annotation task. Results: Several findings regarding appropriate interaction concepts were made, where a key design component was semi-automation based on rapid interaction feedback in a local region. In a user study, the resulting tool was shown to cause substantial speed-up compared to manual work while maintaining quality. Conclusions: The TissueWand tool shows promise to replace manual methods for early stages of dataset curation where no task-specific ML model yet exists to aid the effort.
- Subjects
HISTOPATHOLOGY; ANNOTATIONS; MANUAL labor; MACHINE learning
- Publication
Journal of Pathology Informatics, 2020, Vol 11, p1
- ISSN
2229-5089
- Publication type
Article
- DOI
10.4103/jpi.jpi_5_20