根治性前列腺切除术（前列腺切除术）是临床局限性前列腺癌的标准治疗方法，通常随后进行术后放疗。术后放疗需要在计算机断层扫描 (CT) 图像上准确描绘临床靶区 (CTV) 和淋巴结引流区 (LNA)。然而，CT图像中前列腺切除后单纯的前列腺扩张并不能确定CTV轮廓。受此因素的限制，术后放疗中的手动勾画过程比根治性放疗更加耗时且更具挑战性。此外，CTV和LNA在CT图像中没有可以通过像素值区分的边界，现有的自动分割模型无法得到满意的结果。放射肿瘤学家通常根据有关周围危及器官 (OAR) 的临床共识和指南来确定 CTV 和 LNA 概况。在这项工作中，我们设计了一个级联分割块来明确建立 CTV、LNA 和 OAR 之间的相关性，利用 OAR 特征来指导 CTV 和 LNA 分割。此外，受到自注意力机制和自监督学习成功的启发，我们采用 SwinTransformer 作为骨干，并提出了一个纯粹的基于 SwinTransformer 的具有自监督学习策略的分割网络。我们对所提出的方法进行了广泛的定量和定性评估。与其他竞争性分割模型相比，我们的模型显示出更高的骰子分数和较小的标准偏差，并且详细的可视化结果与地面事实更加一致。我们相信这项工作可以为这一问题提供可行的解决方案，使术后放疗过程更加高效。

Radical prostatectomy (prostate removal) is a standard treatment for clinically localized prostate cancer and is often followed by postoperative radiotherapy. Postoperative radiotherapy requires accurate delineation of the clinical target volume (CTV) and lymph node drainage area (LNA) on computed tomography (CT) images. However, the CTV contour cannot be determined by the simple prostate expansion after resection of the prostate in the CT image. Constrained by this factor, the manual delineation process in postoperative radiotherapy is more time-consuming and challenging than in radical radiotherapy. In addition, CTV and LNA have no boundaries that can be distinguished by pixel values in CT images, and existing automatic segmentation models cannot get satisfactory results. Radiation oncologists generally determine CTV and LNA profiles according to clinical consensus and guidelines regarding surrounding organs at risk (OARs). In this work, we design a cascade segmentation block to explicitly establish correlations between CTV, LNA, and OARs, leveraging OARs features to guide CTV and LNA segmentation. Furthermore, inspired by the success of the self-attention mechanism and self-supervised learning, we adopt SwinTransformer as our backbone and propose a pure SwinTransformer-based segmentation network with self-supervised learning strategies. We performed extensive quantitative and qualitative evaluations of the proposed method. Compared to other competitive segmentation models, our model shows higher dice scores with minor standard deviations, and the detailed visualization results are more consistent with the ground truth. We believe this work can provide a feasible solution to this problem, making the postoperative radiotherapy process more efficient.