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LBMNet:一种混合多尺度的CNN-Mamba框架,用于在MRI中增强3D中风病变细分

Zhejun Kuang^1,2,3, Xingxue Yan^1,2,3, Jiaxuan Yu⁴

¹College of Computer Science and Technology, Changchun University, Changchun, China.

Frontiers in medicine

|February 25, 2026

概括

一个新的CNN-Mamba网络LBMNet通过有效检测小病变来改善脑中风病变细分. 这种新方法在MRI扫描中提高了各种中风病变大小和形态的准确性.

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:

在全球范围内,中风是导致死亡和残疾的主要原因.
从MRI中精确细分脑中风病变对于诊断和治疗至关重要.
现有的方法面临的挑战是病变的变化和检测小病变.

研究的目的:

提出LBMNet,一个新的CNN-Mamba网络,用于准确的脑中风病变细分.
解决当前处理各种病变大小和形态的方法的局限性.
为了改善检测小型中风病变.

主要方法:

开发了LBMNet,这是一个混合CNN-Mamba网络,集成了多尺度卷积编码和基于Mamba的解码.
在编码器中使用了自上而下的LSC模块来进行跨尺度表示.
设计了一个双向空间上下文Mamba (BSC-Mamba) 解码器,具有自适应空间卷积和不对称的自适应封闭特征融合 (BAGF).

主要成果:

在基准数据集上实现了最先进的性能 (Dice:在ATLAS v2.0上67.57%,在ISLES 2022上82.03%).
与现有模型相比,在细分小脑中风病变方面表现出显著的改善.
在各种损伤特征中,LBMNet表现出强大而高效的性能.

结论:

LBMNet为脑中风病变细分提供了一个强大而高效的框架.
拟议的方法显示了改善患者护理的强大临床潜力.
LBMNet有效地处理了中风病变的多样性,特别是小的病变.

关键词:

这就是为什么MRI是MRI.马姆巴·马姆巴是什么意思大脑中风细分脑中风细分深度学习是一种深度学习.混合架构架构是混合架构的架构.医学成像医学成像多个尺度的卷积.国家空间模型国家空间模型

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