Pathological examination has been done manually by visual inspection of hematoxylin and eosin (H&E)-stained images. However, this process is labor intensive, prone to large variations, and lacking reproducibility in the diagnosis of a tumor. We aim to develop an automatic workflow to extract different cell nuclei found in cancerous tumors portrayed in digital renderings of the H&E-stained images. For a given image, we propose a semantic pixel-wise segmentation technique using dilated convolutions. The architecture of our dilated convolutional network (DCN) is based on SegNet, a deep convolutional encoder-decoder architecture. For the encoder, all the max pooling layers in the SegNet are removed and the convolutional layers are replaced by dilated convolution layers with increased dilation factors to preserve image resolution. For the decoder, all max unpooling layers are removed and the convolutional layers are replaced by dilated convolution layers with decreased dilation factors to remove gridding artifacts. We show that dilated convolutions are superior in extracting information from textured images. We test our DCN network on both synthetic data sets and a public available data set of H&E-stained images and achieve better results than the state of the art.

中文翻译：

---

使用膨胀卷积网络自动提取细胞核

通过目视检查苏木精和曙红（H＆E）染色的图像来手动进行病理检查。然而，该过程是劳动密集型的，容易发生大的变化，并且在肿瘤的诊断中缺乏可再现性。我们旨在开发一种自动工作流程，以提取在H＆E染色图像的数字渲染中描绘的癌性肿瘤中发现的不同细胞核。对于给定的图像，我们提出了一种使用膨胀卷积的语义逐像素分割技术。我们的扩展卷积网络（DCN）的体系结构基于SegNet，这是一种深度卷积编码器/解码器体系结构。对于编码器，将删除SegNet中的所有最大池化层，并使用具有增大的膨胀因子的膨胀卷积层替换卷积层，以保留图像分辨率。对于解码器，所有最大解池层都将被删除，而卷积层将被具有减小的扩张因子的扩张卷积层替换，以消除网格化伪影。我们表明，膨胀卷积在从纹理图像中提取信息方面具有优势。我们在合成数据集和H＆E染色图像的公共可用数据集上都测试了DCN网络，并获得了比现有技术更好的结果。