Statistical Shape Models (SSMs) excel at identifying population level anatomical variations, which is at the core of various clinical and biomedical applications, including morphology-based diagnostics and surgical planning. However, the effectiveness of SSM is often constrained by the necessity for expert-driven manual segmentation, a process that is both time-intensive and expensive, thereby restricting their broader application and utility. Recent deep learning approaches enable the direct estimation of Statistical Shape Models (SSMs) from unsegmented images. While these models can predict SSMs without segmentation during deployment, they do not address the challenge of acquiring the manual annotations needed for training, particularly in resource-limited settings. Semi-supervised models for anatomy segmentation can mitigate the annotation burden. Yet, despite the abundance of available approaches, there are no established guidelines to inform end-users on their effectiveness for the downstream task of constructing SSMs. In this study, we systematically evaluate the potential of semi-supervised methods as viable alternatives to manual segmentations for building SSMs. We establish a new performance benchmark by employing various semi-supervised methods for anatomy segmentation under low annotation settings, utilizing the predicted segmentations for the task of SSM. Our results indicate that some methods produce noisy segmentation, which is very unfavorable for SSM tasks, while others can capture the correct modes of variations in the population cohort with 60-80% reduction in required manual annotation

翻译：统计形状模型（SSMs）在识别群体层面的解剖结构变异方面表现出色，这是包括基于形态学的诊断和手术规划在内的各种临床和生物医学应用的核心。然而，SSM的有效性常常受限于需要专家驱动的手动分割，这一过程既耗时又昂贵，从而限制了其更广泛的应用和效用。近期的深度学习方法使得能够直接从非分割图像中估计统计形状模型（SSMs）。尽管这些模型在部署过程中无需分割即可预测SSMs，但它们并未解决获取训练所需手动标注的挑战，尤其是在资源有限的环境中。用于解剖结构分割的半监督模型可以减轻标注负担。然而，尽管现有方法众多，但尚无既定指南来告知最终用户这些方法对于构建SSMs这一下游任务的有效性。在本研究中，我们系统地评估了半监督方法作为手动分割的可行替代方案用于构建SSMs的潜力。通过在低标注设置下采用多种半监督方法进行解剖结构分割，并利用预测的分割结果执行SSM任务，我们建立了一个新的性能基准。我们的结果表明，某些方法会产生噪声分割，这对SSM任务非常不利，而其他方法则能够捕捉群体队列中正确的变异模式，同时所需手动标注量减少了60-80%。