  The lack of large annotated datasets in medical imaging is an intrinsic
burden for supervised Deep Learning (DL) segmentation models. Few-shot learning
approaches are cost-effective solutions to transfer pre-trained models using
only limited annotated data. However, such methods can be prone to overfitting
due to limited data diversity especially when segmenting complex, diverse, and
sparse tubular structures like airways. Furthermore, crafting informative image
representations has played a crucial role in medical imaging, enabling
discriminative enhancement of anatomical details. In this paper, we initially
train a data-driven sparsification module to enhance airways efficiently in
lung CT scans. We then incorporate these sparse representations in a standard
supervised segmentation pipeline as a pretraining step to enhance the
performance of the DL models. Results presented on the ATM public challenge
cohort show the effectiveness of using sparse priors in pre-training, leading
to segmentation Dice score increase by 1% to 10% in full-scale and few-shot
learning scenarios, respectively.
