LiNA: Rethinking Shape Capacity for Efficient Nuclei Analysis

How much shape-decoding capacity does joint nuclei analysis actually need? High-performing systems devote substantial computation to predicting instance geometry, yet it is unclear whether this cost is justified by the complexity of nuclear shape. If nuclear geometry needs only a few degrees of freedom, dense shape decoding may spend much of that computation on capacity with little task-level benefit. We probe this by varying Fourier order N , which controls the boundary’s degrees of freedom. Across four histopathology datasets, a single Fourier harmonic reconstructs ground-truth contours at 85–89% IoU and order N =15 reaches 98.5–99.1%, with an independent boundary-distance measure showing the same low-order saturation. These results raise the possibility that current pipelines devote more capacity to nuclear geometry than the task requires. To test and exploit this observation, we instantiate LiNA (Lightweight Nuclei Analysis), which separates localization and typing from geometry prediction, represents each nucleus with a compact Fourier contour, and strengthens typing through prototype distillation. We then replace its Fourier pathway with a matched dense-mask decoder while keeping the remaining system fixed. Across four backbone scales, the dense-mask decoder improves none of the evaluated task metrics, while computation grows with nucleus density and reaches 6.4× that of the matched Fourier variant on the densest PanNuke tile for ConvNeXt-Small. This compact geometry pathway is nevertheless sufficient for strong joint analysis. At 57M parameters, roughly 8% of 700M-scale SAM-based pipelines, LiNA achieves the best performance on PanNuke and Lizard and the strongest zero-shot results on MoNuSeg and CPM17. Together, these results reveal a shape-capacity mismatch in joint nuclei analysis. LiNA exploits this mismatch to reduce geometry capacity without sacrificing recognition and, combined with its efficient overall design, reaches a new Pareto-efficient operating point among the compared models.