Abstract：
OBJECTIVE: Deep learning can automate delineation in radiation therapy, reducing time and variability. Yet, its efficacy varies across different institutions, scanners, or settings, emphasizing the need for adaptable and robust models in clinical environments. Our study demonstrates the effectiveness of the transfer learning (TL) approach in enhancing the generalizability of deep learning models for auto-segmentation of organs-at-risk (OARs) in cervical brachytherapy.
METHODS: A pre-trained model was developed using 120 scans with ring and tandem applicator on a 3T magnetic resonance (MR) scanner (RT3). Four OARs were segmented and evaluated. Segmentation performance was evaluated by Volumetric Dice Similarity Coefficient (vDSC), 95 % Hausdorff Distance (HD95), surface DSC, and Added Path Length (APL). The model was fine-tuned on three out-of-distribution target groups. Pre- and post-TL outcomes, and influence of number of fine-tuning scans, were compared. A model trained with one group (Single) and a model trained with all four groups (Mixed) were evaluated on both seen and unseen data distributions.
RESULTS: TL enhanced segmentation accuracy across target groups, matching the pre-trained model\'s performance. The first five fine-tuning scans led to the most noticeable improvements, with performance plateauing with more data. TL outperformed training-from-scratch given the same training data. The Mixed model performed similarly to the Single model on RT3 scans but demonstrated superior performance on unseen data.
CONCLUSIONS: TL can improve a model\'s generalizability for OAR segmentation in MR-guided cervical brachytherapy, requiring less fine-tuning data and reduced training time. These results provide a foundation for developing adaptable models to accommodate clinical settings.
METHODS: A pre-trained model was developed using 120 scans with ring and tandem applicator on a 3T magnetic resonance (MR) scanner (RT3). Four OARs were segmented and evaluated. Segmentation performance was evaluated by Volumetric Dice Similarity Coefficient (vDSC), 95 % Hausdorff Distance (HD95), surface DSC, and Added Path Length (APL). The model was fine-tuned on three out-of-distribution target groups. Pre- and post-TL outcomes, and influence of number of fine-tuning scans, were compared. A model trained with one group (Single) and a model trained with all four groups (Mixed) were evaluated on both seen and unseen data distributions.
RESULTS: TL enhanced segmentation accuracy across target groups, matching the pre-trained model\'s performance. The first five fine-tuning scans led to the most noticeable improvements, with performance plateauing with more data. TL outperformed training-from-scratch given the same training data. The Mixed model performed similarly to the Single model on RT3 scans but demonstrated superior performance on unseen data.
CONCLUSIONS: TL can improve a model\'s generalizability for OAR segmentation in MR-guided cervical brachytherapy, requiring less fine-tuning data and reduced training time. These results provide a foundation for developing adaptable models to accommodate clinical settings.
摘要：
目的：深度学习可以在放射治疗中自动化描绘，减少时间和可变性。然而，它的功效因不同机构而异，扫描仪,或设置，强调在临床环境中需要适应性强的模型。我们的研究证明了迁移学习（TL）方法在增强深度学习模型在宫颈近距离放射治疗中对危险器官（OAR）进行自动分割的泛化性方面的有效性。
方法：在3T磁共振（MR）扫描仪（RT3）上使用环形和串联涂药器进行120次扫描，开发了预训练模型。对四个OAR进行了分段和评估。分割性能通过体积骰子相似系数(vDSC)进行评估，95%Hausdorff距离(HD95),表面DSC,并添加路径长度(APL)。该模型在三个分布外的目标群体上进行了微调。前和后TL结果，以及微调扫描次数的影响，进行了比较。在观察到的和未观察到的数据分布上评估用一组训练的模型(单个)和用所有四组训练的模型(混合)。
结果：TL提高了目标群体的分割精度，匹配预训练模型的性能。前五次微调扫描导致了最明显的改进，随着更多数据的增加，性能趋于稳定。在给定相同的训练数据的情况下，TL的性能优于从头开始训练。混合模型在RT3扫描上的表现类似于单一模型，但在看不见的数据上表现出卓越的性能。
结论：TL可以提高MR引导的颈椎近距离放射治疗中OAR分割模型的普适性，需要较少的微调数据和减少的训练时间。这些结果为开发适应临床环境的适应性模型提供了基础。
方法：在3T磁共振（MR）扫描仪（RT3）上使用环形和串联涂药器进行120次扫描，开发了预训练模型。对四个OAR进行了分段和评估。分割性能通过体积骰子相似系数(vDSC)进行评估，95%Hausdorff距离(HD95),表面DSC,并添加路径长度(APL)。该模型在三个分布外的目标群体上进行了微调。前和后TL结果，以及微调扫描次数的影响，进行了比较。在观察到的和未观察到的数据分布上评估用一组训练的模型(单个)和用所有四组训练的模型(混合)。
结果：TL提高了目标群体的分割精度，匹配预训练模型的性能。前五次微调扫描导致了最明显的改进，随着更多数据的增加，性能趋于稳定。在给定相同的训练数据的情况下，TL的性能优于从头开始训练。混合模型在RT3扫描上的表现类似于单一模型，但在看不见的数据上表现出卓越的性能。
结论：TL可以提高MR引导的颈椎近距离放射治疗中OAR分割模型的普适性，需要较少的微调数据和减少的训练时间。这些结果为开发适应临床环境的适应性模型提供了基础。
