Zhang Enguang, Xie Rixin, Bian Yuxin, Wang Jiayan, Tao Pengyi, Zhang Heng, Jiang Shenlu
School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China.
Zhuhai College of Science and Technology, Zhuhai, China.
Heliyon. 2023 Jun 28;9(7):e17647. doi: 10.1016/j.heliyon.2023.e17647. eCollection 2023 Jul.
Cervical cancer diagnosis hinges significantly on precise nuclei segmentation at early stages, which however, remains largely elusive due to challenges such as overlapping cells and blurred nuclei boundaries. This paper presents a novel deep neural network (DNN), the Global Context UNet (GC-UNet), designed to adeptly handle intricate environments and deliver accurate cell segmentation. At the core of GC-UNet is DenseNet, which serves as the backbone, encoding cell images and capitalizing on pre-existing knowledge. A unique context-aware pooling module, equipped with a gating model, is integrated for effective encoding of ImageNet pre-trained features, ensuring essential features at different levels are retained. Further, a decoder grounded in a global context attention block is employed to foster global feature interaction and refine the predicted masks.
宫颈癌的诊断在很大程度上取决于早期精确的细胞核分割，然而，由于细胞重叠和细胞核边界模糊等挑战，这在很大程度上仍然难以实现。本文提出了一种新颖的深度神经网络（DNN），即全局上下文U-Net（GC-UNet），旨在巧妙地处理复杂环境并提供准确的细胞分割。GC-UNet的核心是DenseNet，它作为主干，对细胞图像进行编码并利用已有的知识。集成了一个独特的上下文感知池化模块，该模块配备了一个门控模型，用于对ImageNet预训练特征进行有效编码，确保保留不同层次的关键特征。此外，采用了一个基于全局上下文注意力块的解码器，以促进全局特征交互并细化预测掩码。
