@inproceedings{
xu2024gensegnet,
title={Ge{NS}eg-Net: A General Segmentation Framework for Any Nucleus in Immunohistochemistry Images},
author={Siyuan Xu and Guannan Li and Haofei Song and Jiansheng Wang and Yan Wang and Qingli Li},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=aUt0hjq4Sn}
}
Immunohistochemistry (IHC) plays a crucial role in understanding disease mechanisms, diagnosing pathology and guiding treatment decisions. The precise analysis heavily depends on accurate nucleus segmentation. However, segmentation is challenging due to significant inter- and intra-nucleus variability in morphology and distribution, stemming from inherent characteristics, imaging techniques, tissue differences and other factors. While current deep learning-based methods have shown promising results, their generalization performance is limited, inevitably requiring specific training data. To address the problem, we propose a novel General framework for Nucleus Segmentation in IHC images (GeNSeg-Net). GeNSeg-Net effectively segments nuclei across diverse tissue types and imaging techniques with high variability using a small subset for training. It comprises an enhancement model and a segmentation model. Initially, all nuclei are enhanced to a uniform morphology with distinct features by the enhancement model through generation. The subsequent segmentation task is thereby simplified, leading to higher accuracy. We design a lightweight generator and discriminator to improve both enhancement quality and computational efficiency. Extensive experiments demonstrate the effectiveness of each component within GeNSeg-Net. Compared to existing methods, GeNSeg-Net achieves state-of-the-art (SOTA) segmentation accuracy and generalization performance on both private and public datasets, while maintaining highly competitive processing speed. Code will be available for research and clinical purposes.
GeNSeg-Net: A General Segmentation Framework for Any Nucleus in Immunohistochemistry Images
Siyuan Xu Guannan Li Haofei Song Jiansheng Wang Yan Wang Qingli Li