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This paper introduces napari-TurboBox, a novel napari plugin designed to accelerate interactive bounding box annotation for volumetric bioimaging by addressing the state-sharing limitations of existing tools. By maintaining a single shared copy of bounding boxes across multiple synchronized viewers, napari-TurboBox achieves near-instantaneous updates even with tens of thousands of objects, significantly outperforming previous methods. This optimization is crucial for efficiently preparing large-scale microscopy datasets for promptable segmentation models.
Annotating thousands of objects in 3D microscopy volumes is now 100x faster, enabling promptable segmentation workflows at unprecedented scale.
Promptable segmentation models turn one 3D bounding box into an instance mask. Box placement therefore limits how fast microscopy volumes with thousands of objects are annotated. Each box must be checked in several synchronized viewers, but napari-bbox shares no state between viewers. An edit must update every viewer and slows down with more boxes. napari-TurboBox instead holds one shared copy of the boxes for all viewers. In a four-viewer layout, napari-TurboBox needs a few milliseconds per mouse move to update the three slice viewers up to $10^4$ boxes. The same update in napari-bbox leaves the 33 ms budget beyond about $10^3$ boxes and takes 159 ms at $10^4$. Boxes stay axis-aligned and export to COCO and YOLO as per-slice 2D boxes. napari-TurboBox is a pure-Python plugin for napari 0.6.6 to 0.9. The code is available at https://github.com/N-Friederich/napari-turbobox under the Apache 2.0 licence, and version 0.1.0 is archived at https://doi.org/10.5281/zenodo.23090828 on Zenodo.