Abstract
OBJECTIVE: This study aims to develop and validate SwinHS, a deep learning-based automatic segmentation model designed for precise hippocampus delineation in patients receiving hippocampus-protected whole-brain radiotherapy. By streamlining this process, we seek to significantly improve workflow efficiency for clinicians. METHODS: A total of 100 three-dimensional T1-weighted MR images were collected, with 70 patients allocated for training and 30 for testing. Manual delineation of the hippocampus was performed according to RTOG0933 guidelines. The SwinHS model, which incorporates a 3D ELSA Transformer module and an sSE CNN decoder, was trained and tested on these datasets. To prove the effectiveness of SwinHS, this study compared the segmentation performance of SwinHS with that of V-Net, U-Net, ResNet and VIT. Evaluation metrics included the Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), and Hausdorff distance (HD). Dosimetric evaluation compared radiotherapy plans generated using automatic segmentation (plan AD) versus manual hippocampus segmentation (plan MD). RESULTS: SwinHS outperformed four advanced deep learning-based models, achieving an average DSC of 0.894, a JSC of 0.817, and an HD of 3.430 mm. Dosimetric evaluation revealed that both plan (AD) and plan (MD) met treatment plan constraints for the target volume (PTV). However, the hippocampal D(max) in plan (AD) was significantly greater than that in plan (MD), approaching the 17 Gy constraint limit. Nonetheless, there were no significant differences in D(100%) or maximum doses to other critical structures between the two plans. CONCLUSION: Compared with manual delineation, SwinHS demonstrated superior segmentation performance and a significantly shorter delineation time. While plan (AD) met clinical requirements, caution should be exercised regarding hippocampal D(max). SwinHS offers a promising tool to enhance workflow efficiency and facilitate hippocampal protection in radiotherapy planning for patients with brain metastases.