胶质瘤是一种恶性原发性脑肿瘤，如果不能及时发现，很容易导致死亡。磁共振成像是最常用的诊断胶质瘤的技术，从磁共振图像中精确定位肿瘤区域对医生理解患者情况和制定治疗方案非常重要。然而，依靠放射科医师手动绘制肿瘤是一项繁琐而费时的任务，因此临床上研究自动轮廓描绘胶质瘤区域的方法具有重要的临床意义。为减轻放射科医师描绘肿瘤的沉重负担，我们提出了一种基于最流行的U-Net对称编码器-解码器结构的全卷积网络 XY-Net，用于进行胶质瘤的自动分割。我们为 XY-Net 构建了两个对称子编码器，并在子编码器之间建立了互连的 X 字形特征图传输路径，同时保持每个子编码器和解码器之间的特征图串联。此外，我们在 XY-Net 的训练任务中使用了由平衡交叉熵损失函数和 Dice 损失函数组成的损失函数，以解决医学图像分割任务中的类别不均衡问题。实验结果表明，与单一编码器结构的网络模型相比，所提出的 XY-Net 的 Dice 系数（DC）提高了2.16%，与一些最先进的图像分割方法相比，XY-Net 的性能最佳。我们的方法在测试集上的 DC、HD、召回率和精确度分别为74.49%、10.89 mm、78.06%和76.30%。子编码器和交叉传输路径的结合使模型性能更好；基于这种组合，XY-Net 在 MRIs 的 2D 切片上实现了胶质瘤的端到端自动分割，可以在一定程度上辅助医生掌握病情。© 2023. 国际医学与生物工程联合会。

Glioma is a malignant primary brain tumor, which can easily lead to death if it is not detected in time. Magnetic resonance imaging is the most commonly used technique to diagnose gliomas, and precise outlining of tumor areas from magnetic resonance images (MRIs) is an important aid to physicians in understanding the patient's condition and formulating treatment plans. However, relying on radiologists to manually depict tumors is a tedious and laborious task, so it is clinically important to investigate an automated method for outlining glioma regions in MRIs. To liberate radiologists from the heavy task of outlining tumors, we propose a fully convolutional network, XY-Net, based on the most popular U-Net symmetric encoder-decoder structure to perform automatic segmentation of gliomas. We construct two symmetric sub-encoders for XY-Net and build interconnected X-shaped feature map transmission paths between the sub-encoders, while maintaining the feature map concatenation between each sub-encoder and the decoder. Moreover, a loss function composed of the balanced cross-entropy loss function and the dice loss function is used in the training task of XY-Net to solve the class unevenness problem of the medical image segmentation task. The experimental results show that the proposed XY-Net has a 2.16% improvement in dice coefficient (DC) compared to the network model with a single encoder structure, and compare with some state-of-the-art image segmentation methods, XY-Net achieves the best performance. The DC, HD, recall, and precision of our method on the test set are 74.49%, 10.89 mm, 78.06%, and 76.30%, respectively. The combination of sub-encoders and cross-transmission paths enables the model to perform better; based on this combination, the XY-Net achieves an end-to-end automatic segmentation of gliomas on 2D slices of MRIs, which can play a certain auxiliary role for doctors in grasping the state of illness.© 2023. International Federation for Medical and Biological Engineering.