Understanding Channel Complementarity for Multiplex Cell Segmentation.

Accurate cell segmentation in high-plex imaging remains challenging because no single biomarker channel captures the full structural diversity of cells, and many practical segmentation backbones are trained on a small, fixed-channel input convention. In this work, we study channel complementarity under the standard three-channel input convention of Cellpose-SAM using a systematic fused-evaluation framework with combination-specific operating-point calibration. On a primary healthy salivary gland region, we show that multi-channel gains are selective but meaningful: the strongest pair improves over the single-channel baseline, and the strongest triplet further improves over the best pair. On a second held-out diseased tissue region, we identify strong auxiliary combinations centered on EPCAM, and the triplet combination (DAPI, EPCAM, CD31) remains beneficial across both regions. To test whether these gains are artifacts of conservative post-processing, we additionally perform controlled-merge sensitivity analysis across the merge hyperparameter grid. These analyses do not reproduce the fused triplet gains, suggesting that the observed improvements arise from joint multi-channel inference rather than from the merge heuristic itself. Together, our results show that higher-order channel complementarity is real but selective under the standard three-channel input convention of Cellpose-SAM, and that reproducible triplet benefits can be identified through systematic search.