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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Updated: Jul 8, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

通过数字病理学检测大脑瘤的深度学习驱动的宏观AI细分模型:基于太赫兹成像的AI诊断的基础.

Myeong Suk Yim¹, Yun Heung Kim², Hyeon Sang Bark³

¹Gimhae Biomedical Center, Gimhae Biomedical Industry Promotion Agency (GBIA), Gimhae, 05969, Republic of Korea.

|December 5, 2024

概括

我们使用深度学习开发了一个AI模型,在数字病理学图像中自动识别癌症. 这个工具通过提供指导图像来帮助神经病理学家,以更快,更准确地划分癌症.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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Last Updated: Jul 8, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Published on: January 7, 2019

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科学领域:

数字病理学数字病理学
人工智能的人工智能
在瘤学瘤学.

背景情况:

在数字病理学图像中精确划分癌症,对于诊断和治疗规划至关重要.
手动注释癌症区域是耗时的,需要专门的专业知识.
开发自动化方法可以提高癌症诊断的效率和一致性.

研究的目的:

开发和验证基于深度学习的AI模型,用于在H&E染色的数字病理学图像中自主划分癌症区域.
使用DEEP:PHI和高效的数据处理技术,创建一个强大的和可扩展的AI培训管道.
促进人工智能驱动的癌症诊断技术的发展.

主要方法:

在从转基因脑瘤模型中获取187张H&E染色图像的数据集上利用深度学习算法.
采用DEEP:PHI平台来简化AI模型培训和执行.
实现了用Mask和补丁生成技术进行图像裁剪,以实现数据平衡和资源优化.

主要成果:

成功开发了一种AI模型,能够在数字病理图像中自主细分癌症区域.
人工智能模型提供指导图像,减少了对神经病理学家广泛协助的需求.
策划了一个高质量的大型数据集,支持在癌症诊断中进一步开发AI.

结论:

深度学习为数字病理学中自动化癌症区域划分提供了一种强大的方法.
开发的AI模型提高了诊断效率和准确性,支持神经病理学家.
精选的数据集和方法有助于推进基于AI的癌症诊断,包括太赫兹成像应用.