对于自动脊椎分析的深度学习:对最近的进展进行了一项方法调查
Zhuofan Xie1, Zishan Lin1, Enlong Sun2
1School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen, 361100, China.
概括
本综述总结了脊椎成像自动化脊椎分析 (AVA) 中最近的深度学习进展. 它解决了数据局限性和领域转移等挑战,指导未来对强大的临床系统的研究.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 脊柱外科手术 脊柱外科手术
背景情况:
- 自动脊椎分析 (AVA) 对于计算机辅助诊断,手术规划和脊柱工作流程中的术后评估至关重要.
- 目前的AVA方法面临的挑战包括视野变化,复杂的形态,有限的注释数据和域转移性能退化.
研究的目的:
- 提供最新的,系统的深度学习 (DL) 在自动化脊椎分析 (AVA) 的最新进展的审查.
- 为了解决由AVA中DL方法的快速演变引起的知识差距.
主要方法:
- 综合公开可用的数据集和评估指标,用于标准化的基准测试.
- 分析了最近基于DL的AVA方法,重点关注网络架构改进和学习策略设计.
- 检查了持续存在的技术障碍和新出现的临床需求,从而塑造了未来的研究.
主要成果:
- 确定了AVA的关键挑战,包括数据稀缺性和域泛化.
- 审查了DL在AVA的网络架构和学习范式上的进步.
- 突出了新兴的研究方向,如多式模式学习和基础模型.
结论:
- 本综述提供了当前基于DL的AVA研究的全面综合.
- 它指导未来的研究,以开发强大的,可通用和可临床部署的AVA系统.
- 强调了先进技术的整合,如多式模式学习和智能医疗成像基础模型.
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