QCell: Recombining and Aligning Cell Queries for Overlapping Instance Segmentation
Summary
QCell is a query-based model that improves instance segmentation of overlapping cells in microscopy through latent-space recombination and contrastive query alignment, achieving state-of-the-art results on benchmarks.
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Paper page - QCell: Recombining and Aligning Cell Queries for Overlapping Instance Segmentation
Source: https://huggingface.co/papers/2608.29253
Abstract
QCell is a query-based model that improves instance segmentation of overlapping microscopy cells through latent-space recombination and contrastive query alignment.
Instance segmentation of overlapping cells inmicroscopyremains challenging due to semi-transparent structures that produce weak boundaries and mixed visual evidence in overlap regions. Existing methods address this through local regions of interest or shape priors but lack global reasoning across overlapping objects. We present QCell, a novelquery-based modelthat de-overlaps cell instances inmicroscopyscenes. Our approach combines (i) aninstance recombinationmodule that decomposes and recombines query representations in latent space, enabling the model to reason about complete object structure under overlap, and (ii) acontrastive query alignmentobjective that combines distinctive instance feature learning and separation of overlapping cell queries. We additionally introduce a new Organoid dataset benchmark foroverlapping cell segmentation. We show that QCell outperforms state-of-the-art methods across multiple benchmarks, achieving +2.2 AP and +2.7 AJI on ISBI2014. Code is available at https://github.com/SlavkoPrytula/QCell
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