  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.
