School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences, Shanghai 200050, China.
Genes (Basel). 2022 Feb 26;13(3):431. doi: 10.3390/genes13030431.
Nucleus segmentation of fluorescence microscopy is a critical step in quantifying measurements in cell biology. Automatic and accurate nucleus segmentation has powerful applications in analyzing intrinsic characterization in nucleus morphology. However, existing methods have limited capacity to perform accurate segmentation in challenging samples, such as noisy images and clumped nuclei. In this paper, inspired by the idea of cascaded U-Net (or W-Net) and its remarkable performance improvement in medical image segmentation, we proposed a novel framework called Attention-enhanced Simplified W-Net (ASW-Net), in which a cascade-like structure with between-net connections was used. Results showed that this lightweight model could reach remarkable segmentation performance in the BBBC039 testing set (aggregated Jaccard index, 0.90). In addition, our proposed framework performed better than the state-of-the-art methods in terms of segmentation performance. Moreover, we further explored the effectiveness of our designed network by visualizing the deep features from the network. Notably, our proposed framework is open source.
荧光显微镜的细胞核分割是定量分析细胞生物学测量的关键步骤。自动且准确的细胞核分割在分析细胞核形态的固有特征方面具有强大的应用。然而，现有的方法在处理具有挑战性的样本时，如噪声图像和聚集的细胞核，其分割能力有限。在本文中，受级联 U-Net（或 W-Net）思想及其在医学图像分割方面显著性能提升的启发，我们提出了一种名为 Attention-enhanced Simplified W-Net（ASW-Net）的新框架，其中使用了具有网络间连接的级联结构。结果表明，这个轻量级模型在 BBBC039 测试集中达到了显著的分割性能（聚合 Jaccard 指数为 0.90）。此外，我们提出的框架在分割性能方面优于最先进的方法。此外，我们通过可视化网络的深层特征进一步探索了我们设计的网络的有效性。值得注意的是，我们提出的框架是开源的。
