论文标题
使用分层条件随机场进行胃组织病理学图像分割
Gastric histopathology image segmentation using a hierarchical conditional random field
论文作者
论文摘要
对于应用于胃癌的智能诊断的卷积神经网络（CNN），现有方法主要集中于个人特征或网络框架，而没有政策来描述整体信息。主要是，有条件的随机场（CRF）是一种用于分析包含复杂内容的图像的有效且稳定的算法，可以表征图像中的空间关系。在本文中，提出了一种新型的层级条件随机场（HCRF）基于胃组织病理学图像分割（GHIS）方法，该方法可以自动在光学显微镜获得的胃组织病理学图像中自动定位异常（癌症）区域，以在医疗工作中帮助组织病理学家。该HCRF模型具有高阶电位，包括像素级和贴片级电位，并应用了基于图的后处理以进一步提高其分割性能。特别是，对CNN进行了训练以建立像素级的电位，另外三个CNN经过微调，以建立贴片级潜力，以获得足够的空间分割信息。在实验中，具有560个异常图像的苏木精和曙红（H＆E）染色的胃组织病理学数据集分为1：1：2的训练，验证和测试集，最终，分段准确性，召回准确性，召回和特异性为78.91％，65.59％和81.33％的测试集合，并达到了测试集。我们的HCRF模型表明了高分子性能，并显示了其在GHIS领域的有效性和未来潜力。
For the Convolutional Neural Networks (CNNs) applied in the intelligent diagnosis of gastric cancer, existing methods mostly focus on individual characteristics or network frameworks without a policy to depict the integral information. Mainly, Conditional Random Field (CRF), an efficient and stable algorithm for analyzing images containing complicated contents, can characterize spatial relation in images. In this paper, a novel Hierarchical Conditional Random Field (HCRF) based Gastric Histopathology Image Segmentation (GHIS) method is proposed, which can automatically localize abnormal (cancer) regions in gastric histopathology images obtained by an optical microscope to assist histopathologists in medical work. This HCRF model is built up with higher order potentials, including pixel-level and patch-level potentials, and graph-based post-processing is applied to further improve its segmentation performance. Especially, a CNN is trained to build up the pixel-level potentials and another three CNNs are fine-tuned to build up the patch-level potentials for sufficient spatial segmentation information. In the experiment, a hematoxylin and eosin (H&E) stained gastric histopathological dataset with 560 abnormal images are divided into training, validation and test sets with a ratio of 1 : 1 : 2. Finally, segmentation accuracy, recall and specificity of 78.91%, 65.59%, and 81.33% are achieved on the test set. Our HCRF model demonstrates high segmentation performance and shows its effectiveness and future potential in the GHIS field.
