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Topology-aware few-shot segmentation improves rice leaf-cell boundary and count fidelity, while correction is risky
Gaixing Wei1, Da Zeng1, Shenglin Qin1
1School of Big Data and Artificial Intelligence, Nanning College of Technology, Nanning, Guangxi, China.
Introduction:
Plant-cell measurements require adjacent cells to remain separated, yet few-shot segmentation is usually assessed mainly by foreground overlap.
Methods:
We developed domain-aware topology prompt adaptation (DTPA), which trains a small topology head over a frozen microscopy-specific Segment Anything backbone. We evaluated matched 10-shot segmentation on a leakage-free RiceCell split, conducted a secondary eight-image audit, and assessed label-free routing and selective correction on locked external tests.
Results:
DTPA improved boundary F1 and reduced cell-count error relative to ConvNeXt, while reducing Dice. The boundary advantage persisted across one- to five-pixel tolerances and against two matched instance baselines. The secondary audit linked this trade-off to improved cell-count and typical-cell-area fidelity, but not to every image-derived measurement. The label-free router reduced severe under-counting on a locked PlantSeg root test. Selective correction was less reliable under the tested tissue shifts: 14 of 45 accepted Ovules retries were harmful, and the only accepted floral-meristem retry was harmful.
Discussion:
Topology-aware prompting and routing are computational tools for plant microscopy, not evidence of biological discovery or universal model superiority. A correction-abstention rule derived after the external audit is only a deployment proposal and requires prospective target-domain validation.
