RoMedFormer: A Rotary-Embedding Transformer Foundation Model for 3D Genito-Pelvic Structure Segmentation in MRI and CT
Journal:
arXiv
Published Date:
Mar 18, 2025
Abstract
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.
