Deep learning-based segmentation of genito-pelvic structures in MRI and CT is crucial for applications such as radiation therapy, surgical planning, and disease diagnosis. However, existing segmentation models often struggle with generalizability across imaging modalities, and anatomical variations. In this work, we propose RoMedFormer, a rotary-embedding transformer-based foundation model designed for 3D female genito-pelvic structure segmentation in both MRI and CT. RoMedFormer leverages self-supervised learning and rotary positional embeddings to enhance spatial feature representation and capture long-range dependencies in 3D medical data. We pre-train our model using a diverse dataset of 3D MRI and CT scans and fine-tune it for downstream segmentation tasks. Experimental results demonstrate that RoMedFormer achieves superior performance segmenting genito-pelvic organs. Our findings highlight the potential of transformer-based architectures in medical image segmentation and pave the way for more transferable segmentation frameworks.

翻译：基于深度学习的MRI与CT中生殖-盆腔结构分割对于放射治疗、手术规划和疾病诊断等应用至关重要。然而，现有分割模型在跨影像模态和个体解剖结构差异的泛化能力方面仍面临挑战。本研究提出RoMedFormer，一种基于旋转嵌入Transformer的基础模型，专为MRI和CT中的三维女性生殖-盆腔结构分割而设计。该模型通过自监督学习与旋转位置编码技术，增强了对三维医学影像空间特征的表达能力，并有效捕获长程依赖关系。我们使用包含多源三维MRI与CT扫描的多样化数据集对模型进行预训练，并针对下游分割任务进行微调。实验结果表明，RoMedFormer在生殖-盆腔器官分割任务中取得了优越性能。本研究凸显了Transformer架构在医学图像分割领域的潜力，并为开发更具可迁移性的分割框架奠定了基础。