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.

本文提出了一种基于跨模态融合的注意力适配器细调基础模型来完成多模态 MRI 脑图像下胶质瘤分割任务的方法，该方法优于现有技术方法，具有 88.38％的 Dice 系数和 10.64 的 Hausdorff 距离，从而在分割胶质瘤区域方面呈现出 4％的增长，有助于胶质瘤的治疗。

跨模态注意力适配器：一种对使用多模态脑 MR 图像的 SAM 进行胶质瘤分割微调的方法

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

Human ratings are abstract representations of segmentation quality. To
approximate human quality ratings on scarce expert data, we train surrogate
quality estimation models. We evaluate on a complex multi-class segmentation
problem, specifically glioma segmentation, following the BraTS annotation
protocol. The training data features quality ratings from 15 expert
neuroradiologists on a scale ranging from 1 to 6 stars for various
computer-generated and manual 3D annotations. Even though the networks operate
on 2D images and with scarce training data, we can approximate segmentation
quality within a margin of error comparable to human intra-rater reliability.
Segmentation quality prediction has broad applications. While an understanding
of segmentation quality is imperative for successful clinical translation of
automatic segmentation quality algorithms, it can play an essential role in
training new segmentation models. Due to the split-second inference times, it
can be directly applied within a loss function or as a fully-automatic dataset
curation mechanism in a federated learning setting.

该论文通过训练替代质量评估模型来评估分割质量，其中涉及各个领域的专家数据，并将此方法应用于具有广泛应用前景的分割质量预测。