颅内生殖细胞肿瘤是一种罕见的肿瘤，主要影响儿童和青少年。放射疗法是综合治疗方法的基石。全脑室系统的放射和局部肿瘤的放射可以在确保治愈效果的同时减少放射治疗晚期并发症。然而，手动确定脑室系统对医生来说是耗时且费力的工作。多样化的脑室形状和由脑积水引起的脑室扩张增加了自动分割算法的难度。因此，本研究提出了一种全自动分割框架。首先，我们设计了一种基于无监督学习的新型标签映射器，用于处理脑室形状变化并获取初步分割结果。然后，为了提高框架的分割性能，我们改进了区域生长算法，并结合全连接条件随机场来优化来自区域和体素尺度的初步结果。在只需一组带注释数据的情况下，平均时间成本为153.01秒，平均目标分割准确率可达84.69％。此外，我们还在实际临床应用中验证了该算法。结果表明，我们提出的方法有助于医生确定放射治疗目标，是可行和临床实用的，并且可能填补颅内生殖细胞肿瘤脑室目标自动划定方法的空白。

Intracranial germ cell tumors are rare tumors that mainly affect children and adolescents. Radiotherapy is the cornerstone of interdisciplinary treatment methods. Radiation of the whole ventricle system and the local tumor can reduce the complications in the late stage of radiotherapy while ensuring the curative effect. However, manually delineating the ventricular system is labor-intensive and time-consuming for physicians. The diverse ventricle shape and the hydrocephalus-induced ventricle dilation increase the difficulty of automatic segmentation algorithms. Therefore, this study proposed a fully automatic segmentation framework. Firstly, we designed a novel unsupervised learning-based label mapper, which is used to handle the ventricle shape variations and obtain the preliminary segmentation result. Then, to boost the segmentation performance of the framework, we improved the region growth algorithm and combined the fully connected conditional random field to optimize the preliminary results from both regional and voxel scales. In the case of only one set of annotated data is required, the average time cost is 153.01s, and the average target segmentation accuracy can reach 84.69%. Furthermore, we verified the algorithm in practical clinical applications. The results demonstrate that our proposed method is beneficial for physicians to delineate radiotherapy targets, which is feasible and clinically practical, and may fill the gap of automatic delineation methods for the ventricular target of intracranial germ celltumors.