  According to the 2021 World Health Organization (WHO) Classification scheme
for gliomas, glioma segmentation is a very important basis for diagnosis and
genotype prediction. In general, 3D multimodal brain MRI is an effective
diagnostic tool. In the past decade, there has been an increase in the use of
machine learning, particularly deep learning, for medical images processing.
Thanks to the development of foundation models, models pre-trained with
large-scale datasets have achieved better results on a variety of tasks.
However, for medical images with small dataset sizes, deep learning methods
struggle to achieve better results on real-world image datasets. In this paper,
we propose a cross-modality attention adapter based on multimodal fusion to
fine-tune the foundation model to accomplish the task of glioma segmentation in
multimodal MRI brain images with better results. The effectiveness of the
proposed method is validated via our private glioma data set from the First
Affiliated Hospital of Zhengzhou University (FHZU) in Zhengzhou, China. Our
proposed method is superior to current state-of-the-art methods with a Dice of
88.38% and Hausdorff distance of 10.64, thereby exhibiting a 4% increase in
Dice to segment the glioma region for glioma treatment.
