As an important upstream task for many medical applications, supervised landmark localization still requires non-negligible annotation costs to achieve desirable performance. Besides, due to cumbersome collection procedures, the limited size of medical landmark datasets impacts the effectiveness of large-scale self-supervised pre-training methods. To address these challenges, we propose a two-stage framework for one-shot medical landmark localization, which first infers landmarks by unsupervised registration from the labeled exemplar to unlabeled targets, and then utilizes these noisy pseudo labels to train robust detectors. To handle the significant structure variations, we learn an end-to-end cascade of global alignment and local deformations, under the guidance of novel loss functions which incorporate edge information. In stage II, we explore self-consistency for selecting reliable pseudo labels and cross-consistency for semi-supervised learning. Our method achieves state-of-the-art performances on public datasets of different body parts, which demonstrates its general applicability.
翻译:作为许多医疗应用的重要上游任务,受监督的地标定位仍需要不可忽略的注解成本才能达到理想的性能。 此外,由于繁琐的收集程序,医学地标数据集的有限规模影响了大规模自监督的训练前方法的有效性。为了应对这些挑战,我们提议了一个单点医学地标定位的两阶段框架,首先通过标签的示意图到无标签的目标进行未经监督的注册来推断里程碑,然后利用这些噪音的假标签来训练强健的探测器。为了处理重大的结构变异,我们在包含优势信息的新型损失功能的指导下,学会了全球接轨和局部变形的端至端级。在第二阶段,我们探索了选择可靠的假标签和半监督学习的交叉一致性。我们的方法在不同的身体部分的公共数据集上取得了最先进的表现,显示了其普遍适用性。