FreqPath-Net: Frequency-Aware Spectral-Orthogonal Network for Histopathological Nuclei Segmentation.

Abstract

Nuclei segmentation is a fundamental but challenging task in computational pathology due to diverse morphologies, blurred boundaries, and staining variations. Despite remarkable progress, existing models often suffer from structural instability under morphological and staining variations. We attribute this instability to disrupted frequency-spatial consistency and address it through FreqPath-Net, which enforces frequency-spatial consistency for robust nuclei segmentation. By operating directly in the frequency domain, FreqPath-Net achieves morphology-invariant and stain-robust feature representations. The Spectral Wavelet Attention Module (SWAM) adaptively enhances high-frequency boundary cues while maintaining low-frequency consistency, addressing boundary blurring and detail loss. Furthermore, the Orthogonal Direction-Constrained Frequency Module (ODFM) captures global spectral patterns and enforces directional consistency, effectively preserving boundary orientation and structural integrity by leveraging frequency-spatial consistency. Extensive experiments on twelve nuclei segmentation benchmarks show that FreqPath-Net consistently outperforms state-of-the-art methods. On the multi-organ Pan-Nuke dataset, FreqPath-Net achieves an mIoU of 85.32%, outperforming the second-best method by 2.93%. Code: https://github.com/huangjin520/FreqPath-Net.