■三维头影测量分析在颅颌面评估中至关重要,其中颅颌面(CMF)CT扫描中的标志检测是关键组成部分。然而,为此任务创建强大的深度学习模型通常需要由经验丰富的医疗专业人员注释的大量CMFCT数据集,一个耗时耗力的过程。相反,获取大量未标记的CMFCT数据相对简单。因此,半监督学习(SSL)利用有限的标记数据,辅以足够的未标记数据集,可能是解决这一挑战的可行方案。
■我们开发了一个SSL模型,名为CephaloMatch,基于强-弱扰动一致性框架。所提出的SSL模型通过粗略检测结合了头部位置校正技术,以增强标记和未标记数据集之间的一致性,以及用于扩展扰动空间的多层扰动方法。使用362次CMFCT扫描对所提出的SSL模型进行了评估,分成一个训练集(60次扫描),验证集(14次扫描),和一个未标记的集合(288次扫描)。
■所提出的SSL模型获得了1.60±0.87mm的检测误差,显着超过传统的全监督学习模型的性能(1.94±1.12mm)。值得注意的是,所提出的SSL模型仅使用标记数据集的一半即可实现等效的检测精度(1.91±1.00mm),与完全监督学习模型相比。
■所提出的SSL模型在使用有限的标记CMFCT数据集的地标检测中展示了出色的性能,显着减少医疗专业人员的工作量,并提高3D头影测量分析的准确性。
UNASSIGNED: Three-dimensional cephalometric analysis is crucial in craniomaxillofacial assessment, with landmarks detection in craniomaxillofacial (CMF) CT scans being a key component. However, creating robust deep learning models for this task typically requires extensive CMF CT datasets annotated by experienced medical professionals, a process that is time-consuming and labor-intensive. Conversely, acquiring large volume of unlabeled CMF CT data is relatively straightforward. Thus, semi-supervised learning (SSL), leveraging limited labeled data supplemented by sufficient unlabeled dataset, could be a viable solution to this challenge.
UNASSIGNED: We developed an SSL model, named CephaloMatch, based on a strong-weak perturbation consistency framework. The proposed SSL model incorporates a head position rectification technique through coarse detection to enhance consistency between labeled and unlabeled datasets and a multilayers perturbation method which is employed to expand the perturbation space. The proposed SSL model was assessed using 362 CMF CT scans, divided into a training set (60 scans), a validation set (14 scans), and an unlabeled set (288 scans).
UNASSIGNED: The proposed SSL model attained a detection error of 1.60 ± 0.87 mm, significantly surpassing the performance of conventional fully supervised learning model (1.94 ± 1.12 mm). Notably, the proposed SSL model achieved equivalent detection accuracy (1.91 ± 1.00 mm) with only half the labeled dataset, compared to the fully supervised learning model.
UNASSIGNED: The proposed SSL model demonstrated exceptional performance in landmarks detection using a limited labeled CMF CT dataset, significantly reducing the workload of medical professionals and enhances the accuracy of 3D cephalometric analysis.