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相关实验视频

Updated: Jul 8, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

在老鼠大脑MRI图像中使用编码解码框架进行自动化中风损伤细分.

Herng-Hua Chang, Shin-Joe Yeh, Ming-Chang Chiang

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 12, 2023

概括

一个新的深度学习框架在扩散权重成像 (DWI) 中准确地细分了中风病变,改善了临床前中风研究,并可能有助于临床评分系统.

科学领域:

神经科学是一个神经科学.
医疗成像医学成像
人工智能的人工智能

背景情况:

在全球范围内,中风是导致死亡和长期残疾的主要原因.
在扩散权重成像 (DWI) 中精确细分中风病变对于研究和临床应用至关重要.
在DWI图像中不均的强度和模糊的界限带来了重大细分挑战.

研究的目的:

开发和评估基于深度学习的框架,用于在DWI图像中实现自动化中风病变细分.
为了解决与细分心脏病发作区域的困难,这些区域的强度不均,边界模糊.
为临床前中风调查提供可靠的工具.

主要方法:

设计了一个编码器-解码器深度学习网络,结合了用于多尺度特征提取的混合块.
为了培训和评估细分框架,编制了一个内部的DWI图像数据集.
提出的方法通过广泛的实验与现有技术进行了比较.

主要成果:

开发的框架在DWI图像中实现了中风病变的准确细分.
该系统在质量和数量上表现出优越的性能,与几个竞争方法相比.
深度学习方法有效地应对了强度不均和边界模糊的挑战.

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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Published on: September 25, 2019

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

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Last Updated: Jul 8, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

结论:

拟议的深度学习框架为DWI中自动化中风病变细分提供了强大的解决方案.
这种自动化系统可以通过提供准确的病变识别来显著促进临床前中风研究.
该工具有可能帮助神经科学家开发新的临床评分系统,减少检查时间和提高评审者之间的可靠性.