Identity-Aware U-Net: Fine-grained Cell Segmentation via Identity-Aware Representation Learning
For biomedical image analysis, this work addresses the problem of distinguishing morphologically similar cells, which is critical for accurate cell counting and analysis.
The paper tackles fine-grained segmentation of objects with highly similar shapes, such as cells, by proposing Identity-Aware U-Net (IAU-Net) that jointly models spatial localization and instance discrimination. The method achieves promising results on cell segmentation benchmarks, especially in challenging cases with similar contours and dense layouts.
Precise segmentation of objects with highly similar shapes remains a challenging problem in dense prediction, especially in scenarios with ambiguous boundaries, overlapping instances, and weak inter-instance visual differences. While conventional segmentation models are effective at localizing object regions, they often lack the discriminative capacity required to reliably distinguish a target object from morphologically similar distractors. In this work, we study fine-grained object segmentation from an identity-aware perspective and propose Identity-Aware U-Net (IAU-Net), a unified framework that jointly models spatial localization and instance discrimination. Built upon a U-Net-style encoder-decoder architecture, our method augments the segmentation backbone with an auxiliary embedding branch that learns discriminative identity representations from high-level features, while the main branch predicts pixel-accurate masks. To enhance robustness in distinguishing objects with near-identical contours or textures, we further incorporate triplet-based metric learning, which pulls target-consistent embeddings together and separates them from hard negatives with similar morphology. This design enables the model to move beyond category-level segmentation and acquire a stronger capability for precise discrimination among visually similar objects. Experiments on benchmarks including cell segmentation demonstrate promising results, particularly in challenging cases involving similar contours, dense layouts, and ambiguous boundaries.