The problem. No segmentation is perfect. Transcripts from one cell get assigned to its neighbor, and the result is contamination: a cell’s expression profile is polluted by whatever was next to it. This inflates spurious co-expression and blurs cell-type boundaries — and it persists no matter which segmenter you used.

The idea. MisTIC treats missegmentation as something to detect and correct after the fact, rather than a problem you can only solve upstream. The approach identifies transcripts likely to have been misassigned — using spatial and expression context to flag molecules that don’t fit their assigned cell — and corrects the resulting contamination in the cell-by-gene matrix.

Why it matters. This completes the segmentation cluster: Baysor draws the boundaries, Segger tries to draw them faster, and MisTIC accepts that whatever you drew still leaks and cleans up after it. For a facility, that layered view — and a QC step for the QC step — is exactly the reproducibility mindset. Contamination is a quiet source of false biology; naming and correcting it is worth a slot.

Verdict. A 2025 preprint, so I’m holding its specific claims loosely until peer review and independent testing. Conceptually it fills a real gap. I’d want to see how much correction changes real cell-type calls — and whether it ever over-corrects genuine signal — before leaning on it. One to read closely against the manuscript.