修订的NMS驱动管道用于心脏回和川崎病冠状动脉瘤局部化
Shih-Hsin Chen1, Ho-Chang Kuo2, Ken-Pen Weng3
1Department of Computer Science and Information Engineering, No. 151, Yingzhuan Rd., Tamsui Dist., New Taipei city, 250301, Taiwan.
Computers in biology and medicine
|October 4, 2025
概括
这项研究引入了新的非最大抑制技术 (rNMS-P和rNMS-TP),以增强儿科膜吐和川崎病的心声学对象检测. 这些方法以最小的计算成本提高了准确性,有助于评估心脏图像.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 在心声学中精确地定位儿科膜反和与Kawasaki相关的冠状动脉瘤至关重要,但具有挑战性.
- 目前在心声回声学中的物体检测方法尚未得到广泛采用.
研究的目的:
- 为了提高对特定儿科心脏病的回声心脏学对象检测的准确性.
- 引入非最大抑制 (NMS) 的轻量级变体,以增强YOLO探测器.
主要方法:
- 开发了两个轻量级的NMS变体:rNMS-P (仅推断) 和rNMS-TP (训练和推断).
- 集成的水平和自转意识的定向盒,应用解剖学约束物体定位.
- 修改了YOLO v5到v9探测器,但没有改变它们的骨干.
主要成果:
- rNMS-TP改善了回的平均精度 (mAP@.5) 从79.7%提高到80.5%,以及川崎病的平均精度从85.7%提高到86.5%.
- rNMS-P提供了高达2.1%的绩效提升,在mAP@.5:.95值时获得了高达2.3%的收益.
- 这两种方法都显示了微不足道的计算成本.
结论:
- 拟议的rNMS变体提供了一种实际的方法,以增强对象检测在心声回声学.
- 这些技术促进了可解释的,独立于操作者的心脏图像评估,用于儿科膜反和川崎病.
- 这些方法提供了显著的准确性改进,没有大量的计算开销.
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