Tailored self-supervised pretraining improves brain MRI diagnostic models

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Xinhao Huang , Zihao Wang , Weichen Zhou , Kexin Yang , Kaihua Wen , Haiguang Liu , Shoujin Huang , Mengye Lyu
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引用次数: 0

Abstract

Self-supervised learning has shown potential in enhancing deep learning methods, yet its application in brain magnetic resonance imaging (MRI) analysis remains underexplored. This study seeks to leverage large-scale, unlabeled public brain MRI datasets to improve the performance of deep learning models in various downstream tasks for the development of clinical decision support systems. To enhance training efficiency, data filtering methods based on image entropy and slice positions were developed, condensing a combined dataset of approximately 2 million images from fastMRI-brain, OASIS-3, IXI, and BraTS21 into a more focused set of 250 K images enriched with brain features. The Momentum Contrast (MoCo) v3 algorithm was then employed to learn these image features, resulting in robustly pretrained models specifically tailored to brain MRI. The pretrained models were subsequently evaluated in tumor classification, lesion detection, hippocampal segmentation, and image reconstruction tasks. The results demonstrate that our brain MRI-oriented pretraining outperformed both ImageNet pretraining and pretraining on larger multi-organ, multi-modality medical datasets, achieving a ∼2.8 % increase in 4-class tumor classification accuracy, a ∼0.9 % improvement in tumor detection mean average precision, a ∼3.6 % gain in adult hippocampal segmentation Dice score, and a ∼0.1 PSNR improvement in reconstruction at 2-fold acceleration. This study underscores the potential of self-supervised learning for brain MRI using large-scale, tailored datasets derived from public sources.
量身定制的自我监督预训练改进了脑MRI诊断模型
自监督学习已显示出增强深度学习方法的潜力,但其在脑磁共振成像(MRI)分析中的应用仍有待探索。本研究旨在利用大规模、未标记的公共脑MRI数据集来提高深度学习模型在临床决策支持系统开发的各种下游任务中的性能。为了提高训练效率,研究人员开发了基于图像熵和切片位置的数据过滤方法,将fastMRI-brain、OASIS-3、IXI和BraTS21的约200万张图像组合成一个更集中的250张 K图像集,其中丰富了大脑特征。然后使用动量对比(MoCo) v3算法来学习这些图像特征,从而产生专门针对脑MRI的鲁棒预训练模型。随后,对预训练模型进行肿瘤分类、病变检测、海马分割和图像重建任务的评估。结果表明,我们的大脑mri导向的预训练在更大的多器官、多模态医学数据集上的表现优于ImageNet预训练和预训练,在4类肿瘤分类精度上提高了~ 2.8 %,在肿瘤检测的平均精度上提高了~ 0.9 %,在成人海马分割Dice评分上提高了~ 3.6 %,在2倍加速下重建的PSNR提高了~ 0.1。这项研究强调了使用来自公共资源的大规模定制数据集进行脑MRI自我监督学习的潜力。
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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
期刊介绍: The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.
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