Purpose: This study aims to create a deep learning (DL) model capable of accurately delineating the ventricles, and by extension, the periventricular space (PVS), following the 2021 EPTN Neuro-Oncology Atlas guidelines on T1-weighted contrast-enhanced MRI scans (T1CE). The performance of this DL model was quantitatively and qualitatively compared with an off-the-shelf model. Materials and Methods: An nnU-Net was trained for ventricle segmentation using both CT and T1CE MRI images from 78 patients. Its performance was compared to that of a publicly available pretrained segmentation model, SynthSeg. The evaluation was conducted on both internal (N = 18) and external (n = 18) test sets, with each consisting of paired CT and T1CE MRI images and expert-delineated ground truths (GTs). Segmentation accuracy was assessed using the volumetric Dice Similarity Coefficient (DSC), 95th percentile Hausdorff distance (HD95), surface DSC, and added path length (APL). Additionally, a local evaluation of ventricle segmentations quantified differences between manual and automatic segmentations across both test sets. All segmentations were scored by radiotherapy technicians for clinical acceptability using a 4-point Likert scale. Results: The nnU-Net significantly outperformed the SynthSeg model on the internal test dataset in terms of median [range] DSC, 0.93 [0.86–0.95] vs. 0.85 [0.67–0.91], HD95, 0.9 [0.7–2.5] mm vs. 2.2 [1.7–4.8] mm, surface DSC, 0.97 [0.90–0.98] vs. 0.84 [0.70–0.89], and APL, 876 [407–1298] mm vs. 2809 [2311–3622] mm, all withp< 0.001. No significant differences in these metrics were found in the external test set. However clinical ratings favored nnU-Net segmentations on the internal and external test sets. In addition, the nnU-Net had higher clinical ratings than the GT delineation on the internal and external test set. Conclusions: The nnU-Net model outperformed the SynthSeg model on the internal dataset in both segmentation metrics and clinician ratings. While segmentation metrics showed no significant differences between the models on the external set, clinician ratings favored nnU-Net, suggesting enhanced clinical acceptability. This suggests that nnU-Net could contribute to more time-efficient and streamlined radiotherapy planning workflows.
目的：本研究旨在开发一种深度学习模型，能够依据2021年EPTN神经肿瘤学图谱指南，在T1加权对比增强磁共振成像（T1CE）上精确勾画脑室及脑室周围空间。该深度学习模型的性能与现有通用模型进行了定量与定性比较。 材料与方法：使用78例患者的CT与T1CE MRI图像训练nnU-Net进行脑室分割，并将其性能与公开可用的预训练分割模型SynthSeg进行比较。评估在内部测试集（N=18）和外部测试集（n=18）上进行，每个测试集均包含配对的CT与T1CE MRI图像以及专家勾画的真实分割结果。分割准确性通过体积戴斯相似系数、95%豪斯多夫距离、表面戴斯相似系数和附加路径长度进行评估。此外，对两个测试集进行了脑室分割的局部评估，量化了手动与自动分割之间的差异。所有分割结果由放疗技师采用4点李克特量表进行临床可接受性评分。 结果：在内部测试数据集上，nnU-Net在各项指标上均显著优于SynthSeg模型：中位戴斯相似系数为0.93对0.85，95%豪斯多夫距离为0.9毫米对2.2毫米，表面戴斯相似系数为0.97对0.84，附加路径长度为876毫米对2809毫米，所有比较p值均小于0.001。在外部测试集上，这些指标未见显著差异。然而，临床评分在内部和外部测试集上均更倾向于nnU-Net的分割结果。此外，nnU-Net在内部和外部测试集上的临床评分均高于真实分割结果。 结论：nnU-Net模型在内部数据集上的分割指标和临床评分均优于SynthSeg模型。虽然在外部测试集上分割指标未见显著差异，但临床评分更倾向于nnU-Net，表明其具有更高的临床可接受性。这表明nnU-Net有助于实现更高效、更简化的放疗计划工作流程。
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