Image-guided radiation therapy (IGRT) is the most effective treatment for head and neck cancer. The successful implementation of IGRT requires accurate delineation of organ-at-risk (OAR) in the computed tomography (CT) images. In routine clinical practice, OARs are manually segmented by oncologists, which is time-consuming, laborious, and subjective. To assist oncologists in OAR contouring, we proposed a three-dimensional (3D) lightweight framework for simultaneous OAR registration and segmentation. The registration network was designed to align a selected OAR template to a new image volume for OAR localization. A region of interest (ROI) selection layer then generated ROIs of OARs from the registration results, which were fed into a multiview segmentation network for accurate OAR segmentation. To improve the performance of registration and segmentation networks, a centre distance loss was designed for the registration network, an ROI classification branch was employed for the segmentation network, and further, context information was incorporated to iteratively promote both networks’ performance. The segmentation results were further refined with shape information for final delineation. We evaluated registration and segmentation performances of the proposed framework using three datasets. On the internal dataset, the Dice similarity coefficient (DSC) of registration and segmentation was 69.7% and 79.6%, respectively. In addition, our framework was evaluated on two external datasets and gained satisfactory performance. These results showed that the 3D lightweight framework achieved fast, accurate and robust registration and segmentation of OARs in head and neck cancer. The proposed framework has the potential of assisting oncologists in OAR delineation.

中文翻译：

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用于头颈癌 CT 图像中危险器官同步配准和分割的 3D 轻量级网络


图像引导放射治疗（IGRT）是头颈癌最有效的治疗方法。 IGRT 的成功实施需要在计算机断层扫描 (CT) 图像中准确描绘危及器官 (OAR)。在常规临床实践中，OAR由肿瘤科医生手动分割，费时、费力且主观性强。为了协助肿瘤学家进行 OAR 轮廓绘制，我们提出了一种用于同步 OAR 配准和分割的三维 (3D) 轻量级框架。配准网络旨在将选定的 OAR 模板与新的图像体积对齐以进行 OAR 定位。然后，感兴趣区域 (ROI) 选择层根据配准结果生成 OAR 的 ROI，并将其输入到多视图分割网络中以实现准确的 OAR 分割。为了提高配准和分割网络的性能，为配准网络设计了中心距离损失，为分割网络采用了ROI分类分支，并进一步结合上下文信息以迭代地提高两个网络的性能。使用形状信息进一步细化分割结果以进行最终描绘。我们使用三个数据集评估了所提出框架的配准和分割性能。在内部数据集上，配准和分割的 Dice 相似系数（DSC）分别为 69.7% 和 79.6%。此外，我们的框架在两个外部数据集上进行了评估，并获得了令人满意的性能。这些结果表明，3D 轻量级框架实现了头颈癌 OAR 的快速、准确和稳健的配准和分割。所提出的框架有可能帮助肿瘤学家描绘 OAR。