School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China.
School of Biomedical Engineering, Division of Life Sciences and Medicine, School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China.
Comput Biol Med. 2024 Mar;171:108131. doi: 10.1016/j.compbiomed.2024.108131. Epub 2024 Feb 22.
Morphological features of individual nuclei serve as a dependable foundation for pathologists in making accurate diagnoses. Existing methods that rely on spatial information for feature extraction have achieved commendable results in nuclei segmentation tasks. However, these approaches are not sufficient to extract edge information of nuclei with small sizes and blurred outlines. Moreover, the lack of attention to the interior of the nuclei leads to significant internal inconsistencies. To address these challenges, we introduce a novel Spatial-Frequency Enhancement Network (SFE-Net) to incorporate spatial-frequency features and promote intra-nuclei consistency for robust nuclei segmentation. Specifically, SFE-Net incorporates a distinctive Spatial-Frequency Feature Extraction module and a Spatial-Guided Feature Enhancement module, which are designed to preserve spatial-frequency information and enhance feature representation respectively, to achieve comprehensive extraction of edge information. Furthermore, we introduce the Label-Guided Distillation method, which utilizes semantic features to guide the segmentation network in strengthening boundary constraints and learning the intra-nuclei consistency of individual nuclei, to improve the robustness of nuclei segmentation. Extensive experiments on three publicly available histopathology image datasets (MoNuSeg, TNBC and CryoNuSeg) demonstrate the superiority of our proposed method, which achieves 79.23%, 81.96% and 73.26% Aggregated Jaccard Index, respectively. The proposed model is available at https://github.com/jinshachen/SFE-Net.
个体细胞核的形态特征为病理学家做出准确诊断提供了可靠的基础。现有的基于空间信息进行特征提取的方法在细胞核分割任务中取得了可喜的成果。然而，这些方法不足以提取具有小尺寸和模糊轮廓的细胞核的边缘信息。此外，这些方法对细胞核内部的关注不足，导致了显著的内部不一致性。为了解决这些挑战，我们引入了一种新的空间频率增强网络（SFE-Net），以整合空间频率特征，并促进核内一致性，从而实现稳健的细胞核分割。具体来说，SFE-Net 包含一个独特的空间频率特征提取模块和一个空间引导特征增强模块，旨在分别保留空间频率信息和增强特征表示，以实现边缘信息的全面提取。此外，我们引入了标签引导蒸馏方法，该方法利用语义特征引导分割网络加强边界约束，并学习个体细胞核的核内一致性，从而提高细胞核分割的鲁棒性。在三个公开的组织病理学图像数据集（MoNuSeg、TNBC 和 CryoNuSeg）上进行的广泛实验表明，我们提出的方法具有优越性，分别达到了 79.23%、81.96%和 73.26%的综合杰卡德指数。所提出的模型可在 https://github.com/jinshachen/SFE-Net 上获得。
