In this Letter, a hierarchical conditional random field (HCRF) model-based gastric histopathology image segmentation (GHIS) method is proposed, which can localise abnormal (cancer) regions in gastric histopathology images to assist histopathologists in medical work. First, to obtain pixel-level segmentation information, the authors retrain a convolutional neural network (CNN) to build up their pixel-level potentials. Then, to obtain abundant spatial segmentation information in patch level, they fine tune another three CNNs to build up their patch-level potentials. Thirdly, based on the pixel- and patch-level potentials, their HCRF model is structured. Finally, a graph-based post-processing is applied to further improve their segmentation performance. In the experiment, a segmentation accuracy of 78.91 % is achieved on a haematoxylin and eosin stained gastric histopathological dataset with 560 images, showing the effectiveness and future potential of the proposed GHIS method.

中文翻译：

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用于胃组织病理学图像中多目标分割的分层条件随机场模型

在这封信中，提出了一种基于分层条件随机场（HCRF）模型的胃组织病理学图像分割（GHIS）方法，该方法可以定位胃组织病理学图像中的异常（癌症）区域，以协助组织病理学家进行医疗工作。首先，为了获得像素级分割信息，作者重新训练了一个卷积神经网络 (CNN) 以建立他们的像素级潜力。然后，为了在补丁级别获得丰富的空间分割信息，他们微调另外三个 CNN 以建立他们的补丁级别潜力。第三，基于像素级和补丁级电位，构建了他们的 HCRF 模型。最后，应用基于图的后处理以进一步提高其分割性能。在实验中，分割精度为 78。