Three-dimensional MRI tumor segmentation method based on boundary perception and tumor diagnosis equipment

Abstract

The invention provides a three-dimensional MRI (Magnetic Resonance Imaging) tumor segmentation method based on boundary perception and tumor diagnosis equipment. The method comprises the following steps: acquiring three-dimensional MRI (Magnetic Resonance Imaging) of multiple tumor patients as a tumor data set, wherein the three-dimensional MRI comprises gadolinium-enhanced T1SC slices; performing tumor segmentation by taking the three-dimensional MRI in the tumor data set as input of a first segmentation model to obtain a category label of a tumor in the three-dimensional MRI; wherein the first segmentation model is a model based on a convolutional neural network CNN, the first segmentation model comprises a first model branch and a second model branch, the first model branch is used for predicting a category label, and the second model branch is used for predicting a surface distance field of a tumor based on boundary perception; the surface distance field is used for describing the nearest distance from each voxel to the tumor surface in the three-dimensional MRI, and training and optimization of the first segmentation model are carried out based on training and optimization of the first model branch and the second model branch. 本申请提供了一种基于边界感知的三维MRI肿瘤分割方法及肿瘤诊断设备。该方法包括：获取多位肿瘤病患的三维MRI作为肿瘤数据集，三维MRI包括钆增强的T1SC切片；将肿瘤数据集中的三维MRI作为第一分割模型的输入进行肿瘤分割，得到三维MRI中肿瘤的类别标签；其中，第一分割模型为基于卷积神经网络CNN的模型，第一分割模型中包括第一模型分支和第二模型分支，第一模型分支用于预测类别标签，第二模型分支用于基于边界感知预测肿瘤的表面距离场，表面距离场用于描述三维MRI中每个体素到肿瘤表面的最近距离，第一分割模型的训练和优化基于第一模型分支和第二模型分支的训练和优化进行。