Vockner Sara, Mattke Matthias, Messner Ivan M, Gaisberger Christoph, Zehentmayr Franz, Ellmauer Klarissa, Ruznic Elvis, Karner Josef, Fastner Gerd, Reitsamer Roland, Roeder Falk, Stana Markus
Department of Radiation Therapy and Radiation Oncology, Paracelsus Medical University, 5020 Salzburg, Austria.
Institute of Research and Development of Advanced Radiation Technologies (radART), Paracelsus Medical University, 5020 Salzburg, Austria.
Cancers (Basel). 2025 Feb 1;17(3):485. doi: 10.3390/cancers17030485.
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.
人工智能（AI）应用在放射治疗中越来越普遍，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，它们在术中电子放射治疗（IOERT）中的应用仍然有限。特别是，目前没有人工智能解决方案可用于对使用移动锥形束CT（CBCT）设备采集的CBCT图像进行轮廓勾画。U-Net卷积神经网络架构在医学图像分割方面取得了巨大成功，但在捕捉全局上下文方面仍存在困难。为了提高IOERT中CBCT分割的准确性，研究人员训练并评估了三种不同的人工智能架构。将自然语言处理模型Transformer和xLSTM的特征添加到流行的U-Net架构中，并与标准U-Net和手动分割性能进行比较。这些网络使用从萨尔茨堡放射治疗与放射肿瘤学系接受IOERT的乳腺癌患者获得的55幅CBCT扫描图像进行训练和测试，并使用骰子系数（DSC）作为相似性度量来评估每种架构的分割性能。标准U-Net的平均DSC值为0.83，具有Transformer特征的U-Net为0.88，具有xLSTM的U-Net为0.66。包括Transformer特征的混合U-Net架构实现了最佳的分割精度，平均比标准U-Net提高了5%，而具有xLSTM的U-Net与标准U-Net相比表现较差。借助自动轮廓勾画，可以生成合成CT图像，并解决与基于三维图像的治疗计划耗时性相关的IOERT挑战。
