Accurate segmentation of organs at risk (OARs) is necessary for adaptive head and neck (H&N) cancer treatment planning, but manual delineation is tedious, slow, and inconsistent. A self-channel-and-spatial-attention neural network (SCSA-Net) is developed for H&N OAR segmentation on CT images. To simultaneously ease the training and improve the segmentation performance, the proposed SCSA-Net utilizes the self-attention ability of the network. Spatial and channel-wise attention learning mechanisms are both employed to adaptively force the network to emphasize the meaningful features and weaken the irrelevant features simultaneously. The proposed network was first evaluated on a public dataset, which includes 48 patients, then on a separate serial CT dataset, which contains ten patients who received weekly diagnostic fan-beam CT scans. On the second dataset, the accuracy of using SCSA-Net to track the parotid and submandibular gland volume changes during radiotherapy treatment was quantified. The Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), average surface distance (ASD), and 95% maximum surface distance (95SD) were calculated on the brainstem, optic chiasm, optic nerves, mandible, parotid glands, and submandibular glands to evaluate the proposed SCSA-Net. The proposed SCSA-Net consistently outperforms the state-of-the-art methods on the public dataset. Specifically, compared with Res-Net and SE-Net, which is constructed from squeeze-and-excitation block equipped residual blocks, the DSC of the optic nerves and submandibular glands is improved by 0.06, 0.03 and 0.05, 0.04 by the SCSA-Net. Moreover, the proposed method achieves statistically significant improvements in terms of DSC on all and eight of nine OARs over Res-Net and SE-Net, respectively. The trained network was able to achieve good segmentation results on the serial dataset, but the results were further improved after fine-tuning of the model using the simulation CT images. For the parotids and submandibular glands, the volume changes of individual patients are highly consistent between the automated and manual segmentation (Pearson’s correlation 0.97–0.99). The proposed SCSA-Net is computationally efficient to perform segmentation (sim 2 s/CT).

自适应头颈部 (H&N) 癌症治疗计划需要对风险器官 (OAR) 进行准确分割，但手动划分是乏味、缓慢且不一致的。开发了一种自通道和空间注意神经网络 (SCSA-Net)，用于 CT 图像上的 H&N OAR 分割。为了同时简化训练并提高分割性能，所提出的 SCSA-Net 利用了网络的自注意力能力。空间和通道注意学习机制都用于自适应地迫使网络同时强调有意义的特征并削弱不相关的特征。提议的网络首先在包含 48 名患者的公共数据集上进行评估，然后在单独的串行 CT 数据集上进行评估，该数据集包含每周接受诊断扇形束 CT 扫描的 10 名患者。在第二个数据集上，量化了使用 SCSA-Net 跟踪放射治疗期间腮腺和下颌下腺体积变化的准确性。对脑干、视交叉、视神经、下颌骨、腮腺和颌下腺来评估提议的 SCSA-Net。提出的 SCSA-Net 在公共数据集上始终优于最先进的方法。具体来说，与 Res-Net 和 SE-Net（由配备有挤压和激发块的残余块构成）相比，SCSA-Net 的视神经和下颌下腺的 DSC 分别提高了 0.06、0.03 和 0.05、0.04 . 而且，所提出的方法分别在 Res-Net 和 SE-Net 上的全部和 8 个 OAR 的 DSC 方面实现了统计学上的显着改进。训练后的网络能够在串行数据集上取得良好的分割结果，但在使用模拟 CT 图像对模型进行微调后，结果进一步提高。对于腮腺和颌下腺，个体患者的体积变化在自动和手动分割之间高度一致（Pearson 相关性 0.97-0.99）。所提出的 SCSA-Net 在执行分割（sim 2 s/CT）方面具有计算效率。但在使用模拟 CT 图像对模型进行微调后，结果得到了进一步的改善。对于腮腺和颌下腺，个体患者的体积变化在自动和手动分割之间高度一致（Pearson 相关性 0.97-0.99）。所提出的 SCSA-Net 在执行分割（sim 2 s/CT）方面具有计算效率。但在使用模拟 CT 图像对模型进行微调后，结果得到了进一步的改善。对于腮腺和颌下腺，个体患者的体积变化在自动和手动分割之间高度一致（Pearson 相关性 0.97-0.99）。所提出的 SCSA-Net 在执行分割（sim 2 s/CT）方面具有计算效率。