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在图像导向儿科手术中的超声波成像中对UNeXt进行自动骨表面细分的评估

Jasper M van der Zee¹, Aimon M Rahman², Kevin Klein Gunnewiek¹

¹Department of Pediatric Surgery, Princess Máxima Center for Pediatric Oncology, 3584 CS Utrecht, The Netherlands.

Bioengineering (Basel, Switzerland)

|October 29, 2025

概括

一个新的深度学习模型使用超声波图像准确地对儿科患者的骨表面进行细分. 这项技术改善了所有儿科年龄组的图像引导手术患者登记.

科学领域:

医学成像医学成像
深度学习应用程序深度学习应用程序
儿科外科手术儿科外科手术

背景情况:

在外科导航中,传统的患者登记需要改进,特别是对于未成熟骨结构的儿科患者.
量身定制的细分方法对于优化幼儿手术导航技术至关重要.
十岁以下患者的不成熟骨由于不那么明显的特征,对准确的细分存在独特的挑战.

研究的目的:

开发和评估一个专门针对儿科患者的自动骨表面细分模型.
评估模型在不同儿科年龄组的表现,包括10岁以下的儿童.
为了实现图像导向儿科手术的准确和有效的患者注册.

主要方法:

从16名患者的儿科四肢,骨盆和胸部骨收集了4309张超声波图像.
由技术医生手动注释图像,并由儿科放射科医生验证.
训练了一个UNeXt深度学习模型,用于自动骨表面细分.

主要成果:

获得了0.85 (SD:0.13) 的平均中线子得分和0.78 mm (SD:1.15 mm) 的平均表面距离.
在不同儿科年龄组中表现一致,在10岁以下的患者中没有显著差异.
该模型准确地检测骨表面,促进精确的患者登记.

结论:

开发的细分模型对于儿科骨表面检测是准确和有效的.
该模型在不同年龄组的一致性能使其适合在儿科图像引导手术中基于超声波的患者登记.
这项技术为改善儿科患者手术导航提供了可行的工具.

关键词:

图像指导手术是指导图像的手术.患者登记患者登记儿科瘤手术儿科瘤手术追踪超声波的超声波可以追踪.

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