Cross-view Instance Assembly in Natural-color Tomography

Existing segment-everything methods were built for video, three-dimensional scenes, grayscale CT data, and artificially colored microscopy. Natural-color tomography, which reveals the interior of opaque materials in RGB, is not among these domains. How well those methods transfer to this domain is largely unknown. We propose WINZE, a class-agnostic instance segmentation method designed for natural-color volumes. WINZE reads the volume section by section in three orthogonal orientations with a frozen SAM 2 backbone and assembles the sections into volumetric instances by rule without training. We compared it with seven segment-everything methods on 1,239 manually annotated instances. WINZE ranks first in instance [email protected] in four of the six scored scopes and achieves scores 1.3 to 3.1 times those of the best compared method on the three sets of scored planes. Our results give a reference point for this domain, motivate methods that build the spatial continuity of objects into their assumptions, and suggest that the main difficulty lies in holding each object as one label across sections.