  Segment Anything Model (SAM) fine-tuning has shown remarkable performance in
medical image segmentation in a fully supervised manner, but requires precise
annotations. To reduce the annotation cost and maintain satisfactory
performance, in this work, we leverage the capabilities of SAM for establishing
semi-supervised medical image segmentation models. Rethinking the requirements
of effectiveness, efficiency, and compatibility, we propose a three-stage
framework, i.e., Concatenate, Fine-tuning, and Re-training (CFR). The current
fine-tuning approaches mostly involve 2D slice-wise fine-tuning that disregards
the contextual information between adjacent slices. Our concatenation strategy
mitigates the mismatch between natural and 3D medical images. The concatenated
images are then used for fine-tuning SAM, providing robust initialization
pseudo-labels. Afterwards, we train a 3D semi-supervised segmentation model
while maintaining the same parameter size as the conventional segmenter such as
V-Net. Our CFR framework is plug-and-play, and easily compatible with various
popular semi-supervised methods. Extensive experiments validate that our CFR
achieves significant improvements in both moderate annotation and scarce
annotation across four datasets. In particular, CFR framework improves the Dice
score of Mean Teacher from 29.68% to 74.40% with only one labeled data of LA
dataset.
