论文标题
基于深度学习的多尺度方法，用于肝脏中的分段癌症区域全部幻灯片图像
A deep learning based multiscale approach to segment cancer area in liver whole slide image
论文作者
论文摘要
本文解决了整个幻灯片图像（WSI）中肝癌分割的问题。我们提出了一种基于自动端到端深神经网络算法的多尺度图像处理方法，用于分割癌症区域。构建了七级七级高斯金字塔表示，以便以不同的尺度提供纹理信息。在这项工作中，使用原始图像水平进行训练程序比较了几个神经体系结构。所提出的方法基于U-NET应用于七个级别的各种分辨率（金字塔亚基）。通过投票机制将不同级别的预测组合在一起。最终的分割结果是在原始图像级别生成的。分别采用了部分颜色归一化和加权重叠方法，分别用于预处理和预测。结果表明，与最先进的艺术相比，提出的多尺度方法的有效性更好。
This paper addresses the problem of liver cancer segmentation in Whole Slide Image (WSI). We propose a multi-scale image processing method based on automatic end-to-end deep neural network algorithm for segmentation of cancer area. A seven-levels gaussian pyramid representation of the histopathological image was built to provide the texture information in 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 subsumpling). 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 weighted overlapping method were applied in preprocessing and prediction separately. The results show the effectiveness of the proposed multi-scales approach achieving better scores compared to the state-of-the-art.
