NucleiFormer: A Nuclei Segmentation Model Optimized by Joint Haar Wavelet and Adaptive Feature Calibration
Yulin Chen, Qian Huang, Zhijian Wang, Ziyang Yin, Meng Geng
Abstract
Nucleus segmentation plays a vital role in medical image analysis. However, existing segmentation methods frequently encounter hurdles, such as the loss of crucial image details during downsampling and issues like noise and spatial displacement. In this study, we propose NucleiFormer, where Haar wavelet transforms are employed in the encoder to replace conventional downsampling techniques. Additionally, we utilize an adaptive feature calibration module to align and calibrate features of different scales, reduce feature redundancy, suppress noise, and enhance the spatial awareness of the model. Extensive experiments validate our model’s superior segmentation performance in accurately delineating nucleus boundaries and minimizing errors.
BibTeX
@inproceedings{icassp2025_nucleiformeranuc,
title = {NucleiFormer: A Nuclei Segmentation Model Optimized by Joint Haar Wavelet and Adaptive Feature Calibration},
author = {Yulin Chen and Qian Huang and Zhijian Wang and Ziyang Yin and Meng Geng},
booktitle = {ICASSP 2025},
year = {2025}
}