Enhancing Single-Slice Segmentation with 3D-to-2D Unpaired Scan Distillation

2D single-slice abdominal computed tomography (CT) enables the assessment of body habitus and organ health with low radiation exposure. However, single-slice data necessitates the use of 2D networks for segmentation, but these networks often struggle to capture contextual information effectively. Consequently, even when trained on identical datasets, 3D networks typically achieve superior segmentation results. In this work, we propose a novel 3D-to-2D distillation framework, leveraging pre-trained 3D models to enhance 2D single-slice segmentation. Specifically, we extract the prediction distribution centroid from the 3D representations, to guide the 2D student by learning intra- and inter-class correlation. Unlike traditional knowledge distillation methods that require the same data input, our approach employs unpaired 3D CT scans with any contrast to guide the 2D student model. Experiments conducted on 707 subjects from the single-slice Baltimore Longitudinal Study of Aging (BLSA) dataset demonstrate that state-of-the-art 2D multi-organ segmentation methods can benefit from the 3D teacher model, achieving enhanced performance in single-slice multi-organ segmentation. Notably, our approach demonstrates considerable efficacy in low-data regimes, outperforming the model trained with all available training subjects even when utilizing only 200 training subjects. Thus, this work underscores the potential to alleviate manual annotation burdens.

二维单层腹部计算机断层扫描（CT）能够在低辐射暴露的情况下评估身体体型和器官健康状况。然而，单层数据需要使用二维网络进行分割，但这些网络往往难以有效地捕捉上下文信息。因此，即使在相同的数据集上进行训练，三维网络通常也能取得更优的分割结果。在这项工作中，我们提出了一种新颖的三维到二维的蒸馏框架，利用预训练的三维模型来增强二维单层分割。具体而言，我们从三维表征中提取预测分布质心，通过学习类内和类间相关性来指导二维学生模型。与传统的知识蒸馏方法需要相同的数据输入不同，我们的方法采用任意对比度的未配对三维CT扫描来指导二维学生模型。在来自单层巴尔的摩衰老纵向研究（BLSA）数据集的707个对象上进行的实验表明，最先进的二维多器官分割方法能够从三维教师模型中受益，在单层多器官分割中实现性能提升。值得注意的是，我们的方法在数据量少的情况下表现出相当高的效能，即使仅使用200个训练对象，也优于使用所有可用训练对象训练的模型。因此，这项工作强调了减轻人工标注负担的潜力。