医学图像细分方面的进展:一项综合调查,重点关注腰椎应用
Ahmed Kabil1, Ghada Khoriba1, Mina Yousef1
1Center for Informatics Science, School of Information Technology and Computer Science, Nile University, 12588, Giza, Egypt.
Computers in biology and medicine
|October 7, 2025
概括
本调查提供了医疗图像分割 (MIS) 技术的全面概述,从传统方法到先进的深度学习模型. 它强调了当前的趋势和在开发临床应用强大的细分系统的持续挑战.
科学领域:
- 医学图像分析 医学图像分析
- 计算机视觉 计算机视觉
- 放射学 放射学是一门学科.
背景情况:
- 医学图像分割 (MIS) 对于诊断,治疗计划和疾病监测至关重要.
- 现有的研究跨越了传统的图像处理和现代深度学习方法.
研究的目的:
- 系统地调查和综合医疗图像细分方法.
- 在MIS中弥合传统和深度学习方法之间的差距.
- 识别该领域的新兴趋势和持续挑战.
主要方法:
- 关于传统技术 (值,边缘检测,基于区域的,集群的,基于模型的) 的综合文献综述.
- 对深度学习架构 (CNN,FCN,U-Net,attention,GAN,Transformers) 的深入分析.
- 探索新兴趋势 (混合模型,交叉模式,联合学习,主动学习) 和关于腰椎细分的案例研究.
主要成果:
- 详细介绍了MIS的既定方法和最先进的深度学习模型.
- 新兴趋势解决了诸如有限数据和概括性等关键挑战.
- 一个关于腰椎细分的案例研究提供了具体的见解.
结论:
- 尽管取得了进展,但数据集偏差,域调整,可解释性和临床整合方面的挑战仍然存在.
- 该调查提供了全面的理解,并确定了未来的研究方向.
- 目标是培养更强大,更高效,更适用于临床的MIS系统.
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