■胶质瘤是一种常见于中枢神经系统的原发性恶性颅脑肿瘤。根据研究,术前诊断和充分了解胶质瘤的影像学特征是非常重要的。尽管如此,传统的图像分配和机器智慧的分割方法在神经胶质瘤分割中是不可接受的。该分析探讨了磁共振成像(MRI)脑肿瘤图像作为脑胶质瘤有效分割方法的潜力。
■这项研究使用了来自附属医院的200张MRI图像,并应用了2维残差块UNet(2DResUNet)。使用2×2内核大小(64内核)1步2D卷积(Conv)层从输入图像中提取特征。本研究中实现的2DDDenseUNet模型在UNet架构中引入了ResBlock机制,以及在输入阶段用于数据增强的高斯噪声层,以及用于替换传统2D卷积层的池化层。最后,验证了所提出的协议的性能及其在胶质瘤分割中的有效措施。
■5倍交叉验证评估的结果表明,尽管对Dice评分的评估结果略低,但提出的2DSesUNet和2DDenseUNet结构具有很高的灵敏度。同时,与实验中使用的其他模型相比,本文提出的DM-DA-UNet模型在各项指标上有了显著的改进,提高了模型的可靠性,为临床治疗策略的准确制定提供了参考和依据。本研究中使用的方法显示出比UNet模型更强的特征提取能力。此外,我们的发现表明,在训练过程中使用广义死亡伤害和偏见交叉熵作为损失函数有效地缓解了神经胶质瘤数据的类失衡,并有效地分割了神经胶质瘤。
■基于改进的UNet网络的方法在MRI脑肿瘤纵向分割程序中具有明显的优势。结果表明,我们开发了一个2D残差块UNet,这可以提高胶质瘤分割在临床过程中的结合。
UNASSIGNED: Glioma is a primary malignant craniocerebral tumor commonly found in the central nervous system. According to research, preoperative diagnosis of glioma and a full understanding of its imaging features are very significant. Still, the traditional segmentation methods of image dispensation and machine wisdom are not acceptable in glioma segmentation. This analysis explores the potential of magnetic resonance imaging (MRI) brain tumor images as an effective segmentation method of glioma.
UNASSIGNED: This study used 200 MRI images from the affiliated hospital and applied the 2-dimensional residual block
UNet (2DResUNet). Features were extracted from input images using a 2×2 kernel size (64-kernel) 1-step 2D convolution (Conv) layer. The 2DDenseUNet model implemented in this study incorporates a ResBlock mechanism within the UNet architecture, as well as a Gaussian noise layer for data augmentation at the input stage, and a pooling layer for replacing the conventional 2D convolutional layers. Finally, the performance of the proposed protocol and its effective measures in glioma segmentation were verified.
UNASSIGNED: The outcomes of the 5-fold cross-validation evaluation show that the proposed 2DResUNet and 2DDenseUNet structure has a high sensitivity despite the slightly lower evaluation result on the Dice score. At the same time, compared with other models used in the experiment, the DM-DA-
UNet model proposed in this paper was significantly improved in various indicators, increasing the reliability of the model and providing a reference and basis for the accurate formulation of clinical treatment strategies. The method used in this study showed stronger feature extraction ability than the
UNet model. In addition, our findings demonstrated that using generalized die harm and prejudiced cross entropy as loss functions in the training process effectively alleviated the class imbalance of glioma data and effectively segmented glioma.
UNASSIGNED: The method based on the improved
UNet network has obvious advantages in the MRI brain tumor portrait segmentation procedure. The result showed that we developed a 2D residual block
UNet, which can improve the incorporation of glioma segmentation into the clinical process.