Accurate nuclei segmentation in histopathological images is crucial for cancer diagnosis. Automating this process offers valuable support to clinical experts, as manual annotation is time-consuming and prone to human errors. However, automating nuclei segmentation presents challenges due to uncertain cell boundaries, intricate staining, and diverse structures. In this paper, we present a segmentation approach that combines the U-Net architecture with a DenseNet-121 backbone, harnessing the strengths of both to capture comprehensive contextual and spatial information. Our model introduces the Wavelet-guided channel attention module to enhance cell boundary delineation, along with a learnable weighted global attention module for channel-specific attention. The decoder module, composed of an upsample block and convolution block, further refines segmentation in handling staining patterns. The experimental results conducted on two publicly accessible histopathology datasets, namely Monuseg and TNBC, underscore the superiority of our proposed model, demonstrating its potential to advance histopathological image analysis and cancer diagnosis. The code is made available at: https://github.com/AyushRoy2001/AWGUNET.

翻译：组织病理学图像中准确的细胞核分割对癌症诊断至关重要。由于人工标注耗时且易出错，自动化该过程能为临床专家提供重要支持。然而，由于细胞边界不确定、染色复杂及结构多样，自动化细胞核分割面临诸多挑战。本文提出一种分割方法，将U-Net架构与DenseNet-121主干网络相结合，充分发挥二者优势以捕获全面的上下文与空间信息。本模型引入小波引导通道注意力模块以增强细胞边界描绘，并采用可学习的加权全局注意力模块实现通道特异性注意力。由上采样块和卷积块构成的解码器模块进一步优化了染色模式处理中的分割效果。在Monuseg和TNBC两个公开组织病理学数据集上的实验结果验证了所提模型的优越性，展现了其在推进组织病理学图像分析与癌症诊断方面的潜力。代码已发布于：https://github.com/AyushRoy2001/AWGUNET。