INSA CVL, University of Orléans, PRISME, EA 4229, 18022 Bourges, France.
INSA CVL, University of Orléans, PRISME, EA 4229, 18022 Bourges, France.
Comput Med Imaging Graph. 2021 Jun;90:101923. doi: 10.1016/j.compmedimag.2021.101923. Epub 2021 Apr 15.
This paper addresses the problem of liver cancer segmentation in Whole Slide Images (WSIs). We propose a multi-scale image processing method based on an automatic end-to-end deep neural network algorithm for the segmentation of cancerous areas. A seven-level gaussian pyramid representation of the histopathological image was built to provide the texture information at different scales. In this work, several neural architectures were compared using the original image level for the training procedure. The proposed method is based on U-Net applied to seven levels of various resolutions (pyramidal subsampling). The predictions in different levels are combined through a voting mechanism. The final segmentation result is generated at the original image level. Partial color normalization and the weighted overlapping method were applied in preprocessing and prediction separately. The results show the effectiveness of the proposed multi-scale approach which achieved better scores than state-of-the-art methods.
本文针对全切片图像（WSI）中的肝癌分割问题，提出了一种基于自动端到端深度神经网络算法的多尺度图像处理方法，用于分割癌区。通过构建组织病理学图像的七级高斯金字塔表示，提供不同尺度的纹理信息。在这项工作中，使用原始图像级别比较了几种神经网络架构的训练过程。所提出的方法基于 U-Net 应用于不同分辨率的七个级别（金字塔子采样）。通过投票机制组合不同级别的预测。最终分割结果在原始图像级别生成。在预处理和预测中分别应用了局部颜色归一化和加权重叠方法。结果表明，所提出的多尺度方法是有效的，其得分优于最新方法。
