研究表明，低级别胶质瘤的类型与其形状有关。传统的诊断方法是从MRI中提取肿瘤形状，根据胶质瘤形状与类型的对应关系来诊断胶质瘤的类型。该方法受MRI背景、肿瘤像素大小以及医生专业水平的影响，导致误诊、漏诊。借助深度学习算法，可以自动分割胶质瘤的形状，从而帮助医生更加专注于胶质瘤的诊断，提高诊断效率。使用传统深度学习算法对胶质瘤 MRI 进行分割的准确性有限，从而妨碍了辅助医生诊断的有效性。我们研究的主要目标是通过利用深度学习算法促进医疗从业者对低级别胶质瘤 MRI 的分割。在本研究中，提出了一种结合多注意门的 UNet 胶质瘤分割网络来解决这一局限性。编码部分基于UNet的算法将注意力门融入到网络的层次结构中，抑制不相关区域的特征，减少特征冗余。解码部分，通过在低层和高层特征融合过程中添加注意力门，突出重要特征信息，减少模型参数，提高模型灵敏度和准确度。网络模型对胶质瘤进行图像分割MRI数据集，算法分割的准确率和平均交叉比（mIoU）分别达到99.7%、87.3%、99.7%和87.6%。与UNet、PSPNet和Attention UNet网络模型相比，该网络模型具有明显的优势准确率、mIoU 和损失收敛。它可以作为辅助医生诊断的标准。版权所有 © 2024 Wolters Kluwer Health, Inc. 保留所有权利。

Studies have shown that the type of low-grade glioma is associated with its shape. The traditional diagnostic method involves extraction of the tumor shape from MRIs and diagnosing the type of glioma based on corresponding relationship between the glioma shape and type. This method is affected by the MRI background, tumor pixel size, and doctors' professional level, leading to misdiagnoses and missed diagnoses. With the help of deep learning algorithms, the shape of a glioma can be automatically segmented, thereby assisting doctors to focus more on the diagnosis of glioma and improving diagnostic efficiency. The segmentation of glioma MRIs using traditional deep learning algorithms exhibits limited accuracy, thereby impeding the effectiveness of assisting doctors in the diagnosis. The primary objective of our research is to facilitate the segmentation of low-grade glioma MRIs for medical practitioners through the utilization of deep learning algorithms.In this study, a UNet glioma segmentation network that incorporates multiattention gates was proposed to address this limitation. The UNet-based algorithm in the coding part integrated the attention gate into the hierarchical structure of the network to suppress the features of irrelevant regions and reduce the feature redundancy. In the decoding part, by adding attention gates in the fusion process of low- and high-level features, important feature information was highlighted, model parameters were reduced, and model sensitivity and accuracy were improved.The network model performed image segmentation on the glioma MRI dataset, and the accuracy and average intersection ratio (mIoU) of the algorithm segmentation reached 99.7%, 87.3%, 99.7%, and 87.6%.Compared with the UNet, PSPNet, and Attention UNet network models, this network model has obvious advantages in accuracy, mIoU, and loss convergence. It can serve as a standard for assisting doctors in diagnosis.Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.