Wang Juan, Zhang Zetao, Wu Minghu, Ye Yonggang, Wang Sheng, Cao Ye, Yang Hao
School of Electrical and Electronic Engineering, Hubei University of Technology, Hongshan District, Hubei Province, Wuhan, China; Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, China.
School of Electrical and Electronic Engineering, Hubei University of Technology, Hongshan District, Hubei Province, Wuhan, China.
Comput Biol Med. 2023 Oct 25;167:107622. doi: 10.1016/j.compbiomed.2023.107622.
Nucleus instance segmentation is an important task in medical image analysis involving cell-level pathological analysis and is of great significance for many biomedical applications, such as disease diagnosis and drug screening. However, the high-density and tight-contact between cells is a common feature of most cell images, which poses a great technical challenge for nuclei instance segmentation. The latest research focuses on CNN-based methods for nuclei instance segmentation, which typically rely on bounding box regression and non-maximum suppression to locate nuclei. However, this frequently results in poor local bounding boxes for nuclei that are adhered or clustered together. In response to the challenges of high-density and tight-contact in cellular images, we propose a novel end-to-end nuclei instance segmentation model. Specifically, we first employ the Swin Transformer as the backbone network of our model, which captures global multi-scale information by combining the global modelling capability of transformers and the local modelling capability of convolutional neural networks (CNNs). Additionally, we integrate a graph convolutional feature fusion module (GCFM), that combines deep and shallow features to learn an affinity matrix. The module also adopts graph convolution to guide the network in learning the object-level local information. Finally, we design a hybrid dilated convolution module (HDC) and insert it into the backbone network to enhance the contextual information over a large range. These components assist the network in extracting rich features. The experimental results demonstrate that our algorithm outperforms several state-of-the-art models on the DSB2018 and LIVECell datasets.
细胞核实例分割是医学图像分析中的一项重要任务，涉及细胞水平的病理分析，对许多生物医学应用具有重要意义，如疾病诊断和药物筛选。然而，细胞之间的高密度和紧密接触是大多数细胞图像的共同特征，这给细胞核实例分割带来了巨大的技术挑战。最新研究集中在基于卷积神经网络（CNN）的细胞核实例分割方法上，这些方法通常依靠边界框回归和非极大值抑制来定位细胞核。然而，对于粘连或聚集在一起的细胞核，这常常导致局部边界框效果不佳。针对细胞图像中高密度和紧密接触的挑战，我们提出了一种新颖的端到端细胞核实例分割模型。具体来说，我们首先采用Swin Transformer作为模型的主干网络，它通过结合Transformer的全局建模能力和卷积神经网络（CNN）的局部建模能力来捕获全局多尺度信息。此外，我们集成了一个图卷积特征融合模块（GCFM），它结合深层和浅层特征来学习亲和矩阵。该模块还采用图卷积来引导网络学习对象级别的局部信息。最后，我们设计了一个混合扩张卷积模块（HDC）并将其插入主干网络，以在大范围内增强上下文信息。这些组件有助于网络提取丰富的特征。实验结果表明，我们的算法在DSB2018和LIVECell数据集上优于几个现有的先进模型。
