文章：

基于Transformer与扩展LSTM方法的U-Net变体在术中电子放射治疗中的锥形束CT分割研究

Cone-Beam CT Segmentation for Intraoperative Electron Radiotherapy Based on U-Net Variants with Transformer and Extended LSTM Approaches

原文发布日期：1 February 2025

DOI: 10.3390/cancers17030485

类型: Article

开放获取: 是

英文摘要：

Artificial Intelligence (AI) applications are increasingly prevalent in radiotherapy, including commercial software solutions for automatic segmentation of anatomical structures for 3D Computed Tomography (CT). However, their use in intraoperative electron radiotherapy (IOERT) remains limited. In particular, no AI solution is available for contouring cone beam CT (CBCT) images acquired with a mobile CBCT device. The U-Net convolutional neural network architecture has gained huge success for medical image segmentation but still has difficulties capturing the global context. To increase the accuracy in CBCT segmentation for IOERT, three different AI architectures were trained and evaluated. The features of the natural language processing models Transformer and xLSTM were added to the popular U-Net architecture and compared with the standard U-Net and manual segmentation performance. These networks were trained and tested using 55 CBCT scans obtained from breast cancer patients undergoing IOERT in the department of radiotherapy and radiation oncology in Salzburg, and each architecture’s segmentation performance was assessed using the dice coefficient (DSC) as a similarity measure. The average DSC values were 0.83 for the standard U-Net, 0.88 for the U-Net with transformer features, and 0.66 for the U-Net with xLSTM. The hybrid U-Net architecture, including Transformer features, achieved the best segmentation accuracy, demonstrating an improvement of 5% on average over the standard U-Net, while the U-Net with xLSTM showed inferior performance compared to the standard U-Net. With the help of automatic contouring, synthetic CT images can be generated, and IOERT challenges related to the time-consuming nature of 3D image-based treatment planning can be addressed.

摘要翻译： 

人工智能（AI）在放射治疗中的应用日益广泛，包括用于三维计算机断层扫描（CT）解剖结构自动分割的商业软件解决方案。然而，其在术中电子放射治疗（IOERT）中的应用仍然有限。特别是，目前尚无针对移动锥形束CT（CBCT）设备获取的CBCT图像进行轮廓勾画的AI解决方案。U-Net卷积神经网络架构在医学图像分割领域取得了巨大成功，但在捕捉全局上下文信息方面仍存在困难。为提高IOERT中CBCT分割的准确性，本研究训练并评估了三种不同的AI架构。将自然语言处理模型Transformer和xLSTM的特征融入流行的U-Net架构，并与标准U-Net及人工分割性能进行比较。这些网络使用萨尔茨堡放射治疗与放射肿瘤科接受IOERT的乳腺癌患者55例CBCT扫描数据进行训练和测试，并采用戴斯系数（DSC）作为相似性度量评估各架构的分割性能。平均DSC值显示：标准U-Net为0.83，融合Transformer特征的U-Net为0.88，融合xLSTM特征的U-Net为0.66。包含Transformer特征的混合U-Net架构实现了最佳分割精度，较标准U-Net平均提升5%，而融合xLSTM特征的U-Net性能则逊于标准U-Net。借助自动轮廓勾画技术，可生成合成CT图像，从而解决基于三维图像的治疗计划耗时较长这一IOERT面临的挑战。

原文链接：

Cone-Beam CT Segmentation for Intraoperative Electron Radiotherapy Based on U-Net Variants with Transformer and Extended LSTM Approaches