ArticleJournal of pathology informatics2020
TissueWand, a Rapid Histopathology Annotation Tool.
Article in Journal of pathology informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- MetFinder: A Tool for Automated Quantitation of Metastatic Burden in Histological Sections From Preclinical Models.Pigment cell & melanoma research · 2025Article
- PatchSorter: a high throughput deep learning digital pathology tool for object labeling.NPJ digital medicine · 2024Article
- Quick Annotator: an open-source digital pathology based rapid image annotation tool.The journal of pathology. Clinical research · 2021Article
- Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training.Journal of digital imaging · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecent 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.
methodsIn 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.
resultsSeveral 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.
conclusionsThe 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.
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Registered trials
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