FPNuNet: a frequency-aware prompt-guided network for nuclear segmentation and classification in immunohistochemistry images

Abstract

Abstract Background Accurate nuclear segmentation and classification (NuSC) in immunohistochemistry (IHC)-stained whole-slide images is essential for reliable biomarker quantification in computational pathology. Although existing NuSC methods perform well on Hematoxylin and Eosin images, they frequently underperform on IHC-stained images owing to stain heterogeneity, low nuclear contrast, and scarce biomarker-specific annotations. Prior efforts based on stain normalization or cross-domain knowledge transfer have yielded only marginal improvements under these conditions. To address these limitations, we introduce FPNuNet, a frequency-aware prompt-guided network tailored for NuSC of IHC-stained images. Results FPNuNet extracts multi-source features through 4 parallel encoders: a frozen SAM-based structural encoder and a frozen UNI-based semantic encoder—both adapted via lightweight discrete cosine transform-based prompt generators—together with a wavelet feature encoder and a multi-scale context encoder that capture complementary spatial and frequency-domain descriptors from the raw input. A discrete cosine transform-enabled frequency-aware fusion neck integrates all 4 feature streams with spectral enhancement, and 3 collaborative decoder branches jointly predict binary masks, horizontal–vertical vectors, and nuclear types. FPNuNet is evaluated on CD47-IHCNuSC, a dataset comprising 86 CD47-stained esophageal cancer patches with 18,483 manually annotated nuclei spanning 7 clinically meaningful subtypes. On CD47-IHCNuSC, FPNuNet achieves the best instance-segmentation and aggregate subtype-classification performance among all evaluated methods under challenging IHC staining conditions. Conclusions These results demonstrate the effectiveness of frequency-aware prompt-guided feature learning for nuclear segmentation and classification in IHC-stained images and suggest that FPNuNet provides a promising framework for robust biomarker-oriented computational pathology analysis.