Research advances in the application of deep neural network combined with neuroimaging in traumatic brain injury

Hu Jiawen; Zhu Ruiting; Zhang Ming

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Research advances in the application of deep neural network combined with neuroimaging in traumatic brain injury

更新时间：2025-11-18

- Research advances in the application of deep neural network combined with neuroimaging in traumatic brain injury
- Journal of Chongqing Medical University Pages: 1-5(2025)
- 作者机构：
  
  西安交通大学第一附属医院放射科，西安 710000
- 作者简介：
- 基金信息：
- DOI：
  CLC： R445.1;TP391.41
- Received：15 July 2025，
  
  Published Online：18 November 2025，
- 稿件说明：
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胡佳雯,朱瑞婷,张明.深度神经网络联合神经影像学在脑外伤中的研究进展[J].重庆医科大学学报,

Hu Jiawen,Zhu Ruiting,Zhang Ming.Research advances in the application of deep neural network combined with neuroimaging in traumatic brain injury[J].Journal of Chongqing Medical University,
胡佳雯,朱瑞婷,张明.深度神经网络联合神经影像学在脑外伤中的研究进展[J].重庆医科大学学报, DOI：10.13406/j.cnki.cyxb..

Hu Jiawen,Zhu Ruiting,Zhang Ming.Research advances in the application of deep neural network combined with neuroimaging in traumatic brain injury[J].Journal of Chongqing Medical University, DOI：10.13406/j.cnki.cyxb..

摘要

脑外伤（traumatic brain injury，TBI）是1种常见且复杂的神经系统损伤，涵盖从轻度到重度多种类型。轻度脑外伤虽多缺乏明显影像学表现，但目前研究表明，早期容易漏诊或误判的边缘系统等特定脑区微小病灶可能与伤后长期认知障碍及精神心理问题有关；重度脑外伤常伴有复杂病灶及扭曲结构，对颅内出血等关键病灶的快速定量影像诊断有较高需求。传统神经影像学在病灶边界分割、微小病灶识别等方面存在明显局限，有限时间内采集图像的方式和图像信息则进一步限制了急诊状态快速检出、微小病灶精准识别的需求。近年来，深度神经网络在医学影像自动识别与分析中的应用迅速发展，为脑外伤的高效诊断与动态管理提供了新的解决方案。本文系统综述了深度神经网络联合神经影像学在脑外伤中的应用进展，重点关注颅内出血自动分割、脑微出血（cerebral microbleeds，CMBs）的精准检测、图像质量提升等关键领域，并展望未来的发展方向。

Abstract

Traumatic brain injury（TBI） is a common and complex neurological condition encompassing a wide spectrum from mild to severe injuries. Although mild TBI often shows no significant abnormalities on conventional neuroimaging，recent studies have shown that subtle lesions in specific brain regions，including the limbic system，are easily missed or misinterpreted in the early stage and may be associated with long-term cognitive deficits and neuropsychiatric symptoms，and severe TBI typically presents with complex lesions and distorted brain structures，thus requiring rapid and quantitative imaging assessment for critical pathologies such as intracranial hemorrhage. Traditional neuroimaging approaches face significant limitations in accurate lesion boundary delineation and detection of minute abnormalities，and constraints in imaging speed and quality within a limited time further hinder the rapid identification and precise detection of subtle lesions in emergency settings. In recent years，rapid development has been observed in the application of deep neural networks in automated identification and analysis of medical images，offering new solutions for the efficient diagnosis and dynamic management of TBI. This article systematically reviews the current advances in the application of deep neural networks and neuroimaging in TBI，with a particular focus on key areas including automated intracranial hemorrhage segmentation，precise detection of cerebral microbleeds，and image quality enhancement. This article also discusses the future research directions in this field.

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Department of Neurosurgery,The First Affiliated Hospital of Chongqing Medical University

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