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For more clinical applications of deep learning models for medical imagesegmentation, high demands on labeled data and computational resources must beaddressed. This study proposes a coarse-to-fine framework with two teachermodels and a student model that combines knowledge distillation and crossteaching, a consistency regularization based on pseudo-labels, for efficientsemi-supervised learning. The proposed method is demonstrated on the abdominalmulti-organ segmentation task in CT images under the MICCAI FLARE 2022challenge, with mean Dice scores of 0.8429 and 0.8520 in the validation andtest sets, respectively.

Knowledge Distillation from Cross Teaching Teachers for Efficient Semi-Supervised Abdominal Organ Segmentation in CT