三维MRI扩散模型合成超声跨模态半监督迁移学习分割脑肿瘤

IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yuhua Li , Shan Jiang, Zhiyong Yang, Liwen Wang, Shuangying Wang, Zeyang Zhou
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引用次数: 0

摘要

准确的超声分割对术中脑导航至关重要,可以改善术前MRI与术中超声之间的非刚性配准,补偿脑偏移。然而,有限的超声注释数据阻碍了深度学习方法的应用。鉴于基于脑MRI的医学图像处理的最新进展,通过跨模态翻译将MRI数据集和深度学习模型转移到美国图像研究中可能会潜在地增强智能脑美国图像处理。在本文中,我们提出了一种新的跨模态半监督迁移学习,利用MRI模态中的注释数据从MRI到US。扩散模型利用条件纹理特征和引导互信息,将注释良好的MRI图像转换为分布更接近真实超声图像的合成超声图像。随后,我们采用了一个分割框架,该框架包括通过图像翻译对来自MRI的合成美国图像进行预训练,然后使用混合数据集进行半监督微调,该数据集集成了标记和未标记的超声数据。广泛的评估报告了SL-DDPM与竞争GAN和扩散模型在MRI-US翻译中的效用。实验结果表明,本文提出的迁移学习策略在DSC的分割准确率为93.43±3.72%。通过微调策略和半监督学习的消融研究以及与其他最先进方法的比较,我们的策略的有效性得到了验证。我们的迁移学习策略提高了脑超声分割模型的准确性和泛化性,即使混合训练数据有限,从而帮助外科医生识别病变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain tumor segmentation via cross-modality semi-supervised transfer learning with 3D MRI diffusion model synthetic ultrasound
Accurate ultrasound segmentation is crucial for intraoperative brain navigation and can improve non-rigid registration between preoperative MRI and intraoperative ultrasound, compensating for brain shift. However, limited annotated ultrasound data hinder the application of deep learning methods. Given recent advances in brain MRI-based medical image processing, transferring MRI datasets and deep learning models to US image research via cross-modal translation may potentially enhance intelligent brain US image processing. In this paper, we propose a novel cross-modality semi-supervised transfer learning from MRI to US by leveraging annotated data in the MRI modality. A diffusion model, leveraging conditional texture features and guided mutual information, transforms well-annotated MRI images into synthetic US images with a distribution closer to real US images. Subsequently, we employ a segmentation framework that involves pretraining with synthetic US images derived from MRI through image translation, followed by semi-supervised fine-tuning using a hybrid dataset that integrates both labeled and unlabeled ultrasound data. Extensive assessments are reported on the utility of SL-DDPM against competing GAN and diffusion models in MRI-US translation. The experimental results demonstrate that our proposed transfer learning strategy achieves a segmentation accuracy of DSC of 93.43 ± 3.72 %. The effectiveness of our strategy is validated through ablation studies on fine-tuning strategies and semi-supervised learning, as well as comparisons with other state-of-the-art methods. Our transfer learning strategy enhances the accuracy and generalization of brain ultrasound segmentation models, even with limited hybrid training data, thereby assisting surgeons in identifying lesion.
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来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
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