Abstract: A method for predicting the morphological changes of liver tumor after ablation based on deep learning includes: obtaining a medical image of liver tumor before ablation and a medical image of liver tumor after ablation; preprocessing the medical image of liver tumor before ablation and the medical image of liver tumor after ablation; obtaining a preoperative liver region map, postoperative liver region map, and postoperative liver tumor residual image map; obtaining a transformation matrix by a Coherent Point Drift (CPD) algorithm and obtaining a registration result map according to the transformation matrix; training the network by a random gradient descent method to obtain a liver tumor prediction model; using the liver tumor prediction model to predict the morphological changes of liver tumor after ablation. The method provides the basis for quantitatively evaluating whether the ablation area completely covers the tumor and facilitates the postoperative treatment plan for the patient.

Abstract: A method for predicting the morphological changes of liver tumor after ablation based on deep learning includes: obtaining a medical image of liver tumor before ablation and a medical image of liver tumor after ablation; preprocessing the medical image of liver tumor before ablation and the medical image of liver tumor after ablation; obtaining a preoperative liver region map, postoperative liver region map, and postoperative liver tumor residual image map; obtaining a transformation matrix by a Coherent Point Drift (CPD) algorithm and obtaining a registration result map according to the transformation matrix; training the network by a random gradient descent method to obtain a liver tumor prediction model; using the liver tumor prediction model to predict the morphological changes of liver tumor after ablation. The method provides the basis for quantitatively evaluating whether the ablation area completely covers the tumor and facilitates the postoperative treatment plan for the patient.

Abstract: A crystal, a preparation method and 3D structure of CD147 extracellular region are provided. Such 3D structure is useful in the determination of the active site of CD147 extracellular region by computer modeling or molecular docking method. The crystal and/or 3D structure are useful in a structure-based drug design and the selection of an antibody, a ligand or an interacting molecule of CD 147 extracellular region.

Abstract: A crystal, a preparation method and 3D structure of CD147 extracellular region are provided. Such 3D structure is useful in the determination of the active site of CD147 extracellular region by computer modeling or molecular docking method. The crystal and/or 3D structure are useful in a structure-based drug design and the selection of an antibody, a ligand or an interacting molecule of CD 147 extracellular region.