  For more clinical applications of deep learning models for medical image
segmentation, high demands on labeled data and computational resources must be
addressed. This study proposes a coarse-to-fine framework with two teacher
models and a student model that combines knowledge distillation and cross
teaching, a consistency regularization based on pseudo-labels, for efficient
semi-supervised learning. The proposed method is demonstrated on the abdominal
multi-organ segmentation task in CT images under the MICCAI FLARE 2022
challenge, with mean Dice scores of 0.8429 and 0.8520 in the validation and
test sets, respectively.
