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According to the 2021 World Health Organization (WHO) Classification schemefor gliomas, glioma segmentation is a very important basis for diagnosis andgenotype prediction. In general, 3D multimodal brain MRI is an effectivediagnostic tool. In the past decade, there has been an increase in the use ofmachine learning, particularly deep learning, for medical images processing.Thanks to the development of foundation models, models pre-trained withlarge-scale datasets have achieved better results on a variety of tasks.However, for medical images with small dataset sizes, deep learning methodsstruggle to achieve better results on real-world image datasets. In this paper,we propose a cross-modality attention adapter based on multimodal fusion tofine-tune the foundation model to accomplish the task of glioma segmentation inmultimodal MRI brain images with better results. The effectiveness of theproposed method is validated via our private glioma data set from the FirstAffiliated Hospital of Zhengzhou University (FHZU) in Zhengzhou, China. Ourproposed method is superior to current state-of-the-art methods with a Dice of88.38% and Hausdorff distance of 10.64, thereby exhibiting a 4% increase inDice to segment the glioma region for glioma treatment.

Cross-modality Attention Adapter: A Glioma Segmentation Fine-tuning Method for SAM Using Multimodal Brain MR Images