Annotation-Efficient Task Guidance for Medical Segment Anything
This work addresses the need for annotation-efficient methods in medical imaging, offering a solution that reduces labeling costs and time for clinicians and researchers, though it is incremental as it builds on existing SAM frameworks.
The paper tackles the problem of expensive manual annotation in medical image segmentation by proposing SAM-Mix, a multitask learning framework that uses class activation maps to guide semi-supervised segmentation, achieving a 5.1% Dice improvement with 90% fewer epochs and only 0.04% labeled data on liver segmentation from CT scans.
Medical image segmentation is a key task in the imaging workflow, influencing many image-based decisions. Traditional, fully-supervised segmentation models rely on large amounts of labeled training data, typically obtained through manual annotation, which can be an expensive, time-consuming, and error-prone process. This signals a need for accurate, automatic, and annotation-efficient methods of training these models. We propose SAM-Mix, a novel multitask learning framework for medical image segmentation that uses class activation maps produced by an auxiliary classifier to guide the predictions of the semi-supervised segmentation branch, which is based on the SAM framework. Experimental evaluations on the public LiTS dataset confirm the effectiveness of SAM-Mix for simultaneous classification and segmentation of the liver from abdominal computed tomography (CT) scans. When trained for 90% fewer epochs on only 50 labeled 2D slices, representing just 0.04% of the available labeled training data, SAM-Mix achieves a Dice improvement of 5.1% over the best baseline model. The generalization results for SAM-Mix are even more impressive, with the same model configuration yielding a 25.4% Dice improvement on a cross-domain segmentation task. Our code is available at https://github.com/tbwa233/SAM-Mix.