论文标题
通过注意力执行网络中的数字病理图像中的精确细胞分割
Accurate Cell Segmentation in Digital Pathology Images via Attention Enforced Networks
论文作者
论文摘要
自动细胞分割是计算机辅助诊断（CAD）管道的重要步骤，例如乳腺癌的检测和分级。准确的细胞分割不仅可以帮助病理学家做出更精确的诊断，而且可以节省大量时间和劳动。但是，这项任务遭受了污渍变化，细胞不均匀强度，背景临时和来自不同组织的细胞。为了解决这些问题，我们提出了一个基于空间注意模块和频道注意模块的注意力执行网络（AENET），以将本地特征与全球依赖关系和权重有效渠道自适应地整合在一起。此外，我们引入了一个功能融合分支，以桥接高级和低级功能。最后，将标记控制的流域算法应用于后处理预测的分割图，以减少碎片区域。在测试阶段，我们提出了一种单独的颜色归一化方法来处理污渍变化问题。我们在Monuseg数据集上评估了该模型。与几种先前方法的定量比较证明了我们方法的优越性。
Automatic cell segmentation is an essential step in the pipeline of computer-aided diagnosis (CAD), such as the detection and grading of breast cancer. Accurate segmentation of cells can not only assist the pathologists to make a more precise diagnosis, but also save much time and labor. However, this task suffers from stain variation, cell inhomogeneous intensities, background clutters and cells from different tissues. To address these issues, we propose an Attention Enforced Network (AENet), which is built on spatial attention module and channel attention module, to integrate local features with global dependencies and weight effective channels adaptively. Besides, we introduce a feature fusion branch to bridge high-level and low-level features. Finally, the marker controlled watershed algorithm is applied to post-process the predicted segmentation maps for reducing the fragmented regions. In the test stage, we present an individual color normalization method to deal with the stain variation problem. We evaluate this model on the MoNuSeg dataset. The quantitative comparisons against several prior methods demonstrate the superiority of our approach.
