Chinese Visible Human (CVH) data sets have been widely used in anatomical teaching and scientific research. Based on true-color, thin-thickness, and high-resolution images which are much more superior than computed tomography, magnetic resonance imaging, and ultrasound, human organs have been segmented and three-dimensional (3D) reconstructed, and the organs have higher accuracy and more detailed information, which makes complex anatomical structures simplified, and makes abstract anatomical structure visualization. Through CVH and their 3D models, researchers got much more anatomical new finding and understanding about human anatomy, which can update anatomical reference books and atlas, and can provide more human morphological information for medical students, surgeons, and anatomists. Here, we will provide a brief summary of the CVH data sets and its applications in teaching and research in recent years.

Background: The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis. Method: In this paper, based on the study of computer-aided diagnosis (CAD) system, graph-based features are applied to gastric cancer histopathology microscopic image analysis, and a classifier is used to classify gastric cancer cells from benign cells. Firstly, image segmentation is performed. After finding the region, cell nuclei are extracted using the k-means method, the minimum spanning tree (MST) is drawn, and graph-based features of the MST are extracted. The graph-based features are then put into the classifier for classification. Result: Different segmentation methods are compared in the tissue segmentation stage, among which are Level-Set, Otsu thresholding, watershed, SegNet, U-Net and Trans-U-Net segmentation; Graph-based features, Red, Green, Blue features, Grey-Level Co-occurrence Matrix features, Histograms of Oriented Gradient features and Local Binary Patterns features are compared in the feature extraction stage; Radial Basis Function (RBF) Support Vector Machine (SVM), Linear SVM, Artificial Neural Network, Random Forests, k-NearestNeighbor, VGG16, and Inception-V3 are compared in the classifier stage. It is found that using U-Net to segment tissue areas, then extracting graph-based features, and finally using RBF SVM classifier gives the optimal results with 94.29%. Conclusion: This paper focus on a graph-based features microscopic image analysis method for gastric cancer histopathology. The final experimental data shows that our analysis method is better than other methods in classifying histopathological images of gastric cancer.