TissueWand, a Rapid Histopathology Annotation Tool
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Published on: March 29, 2021
Martin Lindvall1,2,3, Alexander Sanner1, Fredrik Petré1
1Sectra AB, Research Department, Linköping, Sweden.
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.
