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Updated: Jan 20, 2026

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从CNN到SAM:对CT图像中肝脏瘤细分的深度学习技术的调查

Neman Abdoli¹, Youkabed Sadri¹, Patrik Gilley¹

¹School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.

IEEE access : practical innovations, open solutions

|January 19, 2026

概括

使用深度学习 (DL) 模型进行准确的肝脏瘤细分,自动化了关键的临床任务. 本综述分析了来自CT图像的肝脏瘤细分DL的100多篇论文,突出了趋势和局限性.

科学领域:

医学成像分析医学成像分析
人工智能在医学中的应用
放射学放射学是指放射学

背景情况:

准确的肝脏瘤细分对于临床评估,治疗计划和精准医学至关重要.
传统的细分方法是劳动密集型,耗时,并且对于大规模或实时应用不切实际.
挑战包括瘤多样性,器官相似性,模糊边界和可变图像采集.

研究的目的:

从计算机断层扫描 (CT) 图像提供基于深度学习 (DL) 的方法进行肝脏瘤细分的全面审查.
分析了100多篇关于这个主题的研究论文,检查了输入数据,模型架构和评估指标.
突出开创性贡献,新兴趋势,方法影响和当前DL方法的局限性.

主要方法:

对100多篇研究论文进行系统的文献审查.
在CT图像中分析基于DL的肝脏瘤细分技术.
基于输入数据,模型架构和评估指标的研究分类.

主要成果:

深度学习模型在自动化肝脏瘤细分方面显示出显著的前景.
该审查确定了DL模型架构和数据利用这项任务的关键趋势.
各种方法选择会影响细分性能,但仍存在一些局限性.

结论:

深度学习为自动化具有挑战性的肝脏瘤细分任务提供了强大的解决方案.
需要进一步的研究来解决临床应用当前DL模型的局限性.
本综述为医学图像分析领域的研究人员和临床医生提供了有价值的概述.

关键词:

人工智能的人工智能是人工智能.在CT成像中使用CT成像.深度学习是一种深度学习.肝脏瘤细分肝脏瘤细分

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