自动检测,分类和细分形MRI图像,用于诊断腰椎间椎间盘缩
Md Abu Sayed1, G M Mahmudur Rahman2, Md Sherajul Islam3
1Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh.
Scientific reports
|January 2, 2025
概括
这项研究引入了一种使用YOLOv8和加权平均组合模型的自动化系统,用于从MRI图像中诊断腰间椎间盘 (PLID),从而提高诊断准确性和效率.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 磁共振 (MR) 影像对于诊断腰椎间椎间盘 (PLID) 脱落至关重要.
- 在MRI图像中自动检测和分析PLID对计算机辅助诊断 (CAD) 系统提出了重大挑战.
- 准确识别病理异常对于有效治疗患者至关重要.
研究的目的:
- 开发一个全面的模型,用于自动检测和切割感兴趣的区域 (ROI) 在sagittalMRI图像用于PLID诊断.
- 实施加权平均组合 (WAE) 分类和细分模型,以提高诊断准确度.
- 使用先进的人工智能技术提高PLID评估的效率和精度.
主要方法:
- 利用YOLOv8框架,从MRI图像中准确检测和切割腰部区域和脊椎盘.
- 开发和应用加权平均整体 (WAE) 模型,用于分类和细分任务.
- 基于ROI的综合方法来完善个别诊断模型的性能.
主要成果:
- 对于腰部区域 (mAP50 = 99.50%) 和椎间盘 (mAP50 = 99.40%),YOLOv8实现了高检测准确度.
- 在WAE分类模型中,分类准确率达到了97.64%.
- WAE细分模型获得了95.72%的Dice得分,表明了异常的精确划分.
结论:
- 拟议的综合模型有效地自动化了MR图像中PLID的检测,分类和细分.
- 整合YOLOv8和WAE模型显著提高了诊断准确性和效率.
- 这种自动化技术为PLID的临床评估提供了有前途的进步.
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