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深度学习用于脑血管阻塞细分:一种新的ConvNeXtV2和GRN集成的U-Net框架用于扩散加权成像

Suat Ince¹, Ismail Kunduracioglu², Ali Algarni³

¹Department of Radiology, University of Health Sciences, Van Education and Research Hospital, 65000 Van, Turkey.

|April 9, 2025

概括

这项研究引入了一种先进的U-Net模型,用于在MRI扫描中对脑血管闭塞进行细分. 新的架构提高了准确性和效率,有助于更快的中风诊断和治疗规划.

科学领域:

医疗成像医学成像
人工智能的人工智能
神经学神经学

背景情况:

大脑血管封闭会导致中风和永久的神经损伤.
在磁共振成像 (MRI) 中精确细分脑损伤对于治疗至关重要.
在MRI细分方面的挑战包括低对比度,噪音和复杂的病变结构.

研究的目的:

开发一种新的深度学习模型,用于MRI中准确的脑血管阻塞细分.
解决现有方法的局限性,包括计算成本和细分小或不规则的病变.
增强计算机辅助诊断 (CAD) 系统用于中风检测.

主要方法:

提出了一个新的U-Net架构,包括ConvNeXtV2块和基于GRN的多层感知器 (MLP).
该模型应用于脑血管封闭细分,并进行预处理以去除小病变 (≤5像素).
在ISLES 2022数据集上进行了实验.

主要成果:

拟议的模型实现了0.8015的高交点在联盟 (IoU) 和0.8894.8的子系数.
在细分精度上显著改善,特别是在低对比度地区.
展示了适合临床应用的高计算效率.

结论:

新的U-Net架构与ConvNeXtV2和MLP为大脑血管阻塞细分提供了卓越的性能.
该模型为临床诊断和治疗规划提供了一个有希望,准确和高效的解决方案.
这项工作代表了ConvNeXtV2在这种特定的医学成像任务中的首次应用.

关键词:

脑中风的诊断脑中风的诊断深度学习是一种深度学习.卫生健康卫生健康卫生健康医疗图像处理医学图像处理这就是U-Net.

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