BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation.

ArXiv Pub Date : 2024-11-07
Joseph Cox, Peng Liu, Skylar E Stolte, Yunchao Yang, Kang Liu, Kyle B See, Huiwen Ju, Ruogu Fang
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Abstract

The burgeoning field of brain health research increasingly leverages artificial intelligence (AI) to interpret and analyze neurological data. This study introduces a novel approach towards the creation of medical foundation models by integrating a large-scale multi-modal magnetic resonance imaging (MRI) dataset derived from 41,400 participants in its own. Our method involves a novel two-stage pretraining approach using vision transformers. The first stage is dedicated to encoding anatomical structures in generally healthy brains, identifying key features such as shapes and sizes of different brain regions. The second stage concentrates on spatial information, encompassing aspects like location and the relative positioning of brain structures. We rigorously evaluate our model, BrainFounder, using the Brain Tumor Segmentation (BraTS) challenge and Anatomical Tracings of Lesions After Stroke v2.0 (ATLAS v2.0) datasets. BrainFounder demonstrates a significant performance gain, surpassing the achievements of the previous winning solutions using fully supervised learning. Our findings underscore the impact of scaling up both the complexity of the model and the volume of unlabeled training data derived from generally healthy brains, which enhances the accuracy and predictive capabilities of the model in complex neuroimaging tasks with MRI. The implications of this research provide transformative insights and practical applications in healthcare and make substantial steps towards the creation of foundation models for Medical AI. Our pretrained models and training code can be found at https://github.com/lab-smile/GatorBrain.

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BrainFounder:为神经图像分析建立大脑基础模型。
蓬勃发展的脑健康研究领域越来越多地利用人工智能(AI)来解释和分析神经学数据。本研究介绍了一种创建医学基础模型的新方法,该方法整合了来自 4.14 万名参与者的大规模多模态磁共振成像(MRI)数据集。我们的方法包括使用视觉转换器的新型两阶段预训练方法。第一阶段致力于编码一般健康大脑的解剖结构,识别不同脑区的形状和大小等关键特征。第二阶段专注于空间信息,包括大脑结构的位置和相对定位等方面。我们使用脑肿瘤分割(BraTS)挑战赛和中风后病变解剖追踪 v2.0(ATLAS v2.0)数据集对我们的模型 BrainFounder 进行了严格评估。BrainFounder 的性能大幅提升,超过了之前使用完全监督学习的获胜解决方案。我们的研究结果凸显了提高模型复杂度和来自一般健康大脑的未标记训练数据量的影响,这提高了模型在复杂的核磁共振成像神经成像任务中的准确性和预测能力。这项研究的意义在于为医疗保健领域提供了变革性的见解和实际应用,并为医学人工智能基础模型的创建迈出了实质性的一步。我们的预训练模型和训练代码见 https://github.com/lab-smile/GatorBrain。
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