论文标题
全自动脑肿瘤分段的数据增强方法
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation
论文作者
论文摘要
自动分割神经胶质瘤及其子区域对于诊断，治疗和监测疾病具有重要意义。在本文中，提出了一种称为Tensormixup的增强方法，并应用于三维U-NET结构进行脑肿瘤分割。主要思想包括，首先，根据任何两名具有相同模态的患者的磁共振成像数据的地面真相标签的信息，选择了三个维度的两个图像斑块，三个维度的大小为128个。接下来，使用Beta分布独立采样所有元素的张量被用来混合图像贴片。然后将张量映射到矩阵中，该基质用于混合上述图像贴片的单热编码标签。因此，合成了一个新图像及其单热编码标签。最后，新数据用于训练可用于细分神经胶质瘤的模型。实验结果表明，整个肿瘤，肿瘤核心和增强肿瘤分割的骰子得分的平均精度分别为91.32％，85.67％和82.20％，这证明了拟议的张力符号是可行的，并且对脑肿瘤分割有效。
Automatic segmentation of glioma and its subregions is of great significance for diagnosis, treatment and monitoring of disease. In this paper, an augmentation method, called TensorMixup, was proposed and applied to the three dimensional U-Net architecture for brain tumor segmentation. The main ideas included that first, two image patches with size of 128 in three dimensions were selected according to glioma information of ground truth labels from the magnetic resonance imaging data of any two patients with the same modality. Next, a tensor in which all elements were independently sampled from Beta distribution was used to mix the image patches. Then the tensor was mapped to a matrix which was used to mix the one-hot encoded labels of the above image patches. Therefore, a new image and its one-hot encoded label were synthesized. Finally, the new data was used to train the model which could be used to segment glioma. The experimental results show that the mean accuracy of Dice scores are 91.32%, 85.67%, and 82.20% respectively on the whole tumor, tumor core, and enhancing tumor segmentation, which proves that the proposed TensorMixup is feasible and effective for brain tumor segmentation.
