GBT-SAM: 一种参数高效、具有通用性的脑肿瘤分割模型，适用于多参数磁共振成像
GBT-SAM: A Parameter-Efficient Depth-Aware Model for Generalizable Brain tumour Segmentation on mp-MRI
AI总结：
GBT-SAM是一种参数高效、具有通用性的脑肿瘤分割模型，适用于多参数磁共振成像，通过两步微调策略和深度感知模块实现高效分割。
摘要：
胶质瘤是一种侵袭性强的脑肿瘤，需要准确的影像学诊断，分割在评估形态和治疗决策中起着关键作用。手动勾勒胶质瘤耗时且易产生变异，促使使用深度学习来提高一致性并减轻临床工作量。然而，现有方法往往未能充分利用多参数磁共振成像（mp-MRI）中的可用信息，特别是切片间的上下文特征，并且通常需要大量的计算资源，同时在肿瘤类型变化时缺乏鲁棒性。我们提出了GBT-SAM，一种参数高效的深度学习框架，该框架适应了Segment Anything Model（SAM），一种大规模视觉模型，用于体积化的mp-MRI数据。GBT-SAM通过选择每个扫描中少于2.6%的切片来减少输入复杂性，同时整合所有四种MRI模态，以最小的成本保留必要的肿瘤相关信息。此外，我们的模型通过两步微调策略进行训练，该策略结合了深度感知模块以捕捉切片间的相关性以及轻量级适应层，最终仅需6.5M可训练参数，这是SAM基方法中最低的。GBT-SAM在BraTS成人胶质瘤数据集上实现了93.54的Dice分数，并在脑膜瘤、儿童胶质瘤和撒哈拉以南胶质瘤数据集上展示了鲁棒性能。这些结果突显了GBT-SAM作为计算高效且领域鲁棒的框架的潜力，用于使用mp-MRI进行脑肿瘤分割。我们的代码和模型可在https://github.com/vpulab/med-sam-brain上获得。
英文摘要：
Gliomas are aggressive brain tumors that require accurate imaging-based diagnosis, with segmentation playing a critical role in evaluating morphology and treatment decisions. Manual delineation of gliomas is time-consuming and prone to variability, motivating the use of deep learning to improve consistency and alleviate clinical workload. However, existing methods often fail to fully exploit the information available in multi-parametric MRI (mp-MRI), particularly inter-slice contextual features, and typically require considerable computational resources while lacking robustness across tumor type variations. We present GBT-SAM, a parameter-efficient deep learning framework that adapts the Segment Anything Model (SAM), a large-scale vision model, to volumetric mp-MRI data. GBT-SAM reduces input complexity by selecting fewer than 2.6\% of slices per scan while incorporating all four MRI modalities, preserving essential tumor-related information with minimal cost. Furthermore, our model is trained by a two-step fine-tuning strategy that incorporates a depth-aware module to capture inter-slice correlations and lightweight adaptation layers, resulting in just 6.5M trainable parameters, which is the lowest among SAM-based approaches. GBT-SAM achieves a Dice Score of 93.54 on the BraTS Adult Glioma dataset and demonstrates robust performance on Meningioma, Pediatric Glioma, and Sub-Saharan Glioma datasets. These results highlight GBT-SAM's potential as a computationally efficient and domain-robust framework for brain tumor segmentation using mp-MRI. Our code and models are available at https://github.com/vpulab/med-sam-brain .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
