Browsing by Subject "foundation models"

Foundation models have the potential to reduce the level of supervision required for medical image segmentation tasks. Currently, the medical image segmentation field still largely relies on supervised, task specific models. The aim of this thesis is to investigate if a foundation model, the Segment Anything Model (SAM), can be used to reduce the level of supervision needed for medical image segmentation. The main goal of this thesis is to see if the annotation workload required to generate labeled medical segmentation datasets can be significantly reduced with the help of Segment Anything Model. The second goal of this thesis is to validate the zero-shot performance of the Segment Anything Model on a medical segmentation dataset. A UNet model is used as a baseline. The results of this thesis give positive feedback on SAM's ability to be used as a tool for medical image annotation. During the experiments, it was found that especially for homogeneous, clearly outlined tasks, like organs, using ''pseudo labels'' generated by SAM for training a UNet model resulted in comparable accuracy with training a UNet model on human-annotated labels. Furthermore, the results show that zero-shot SAM has somewhat comparable performance to UNet, and even beats UNet in two of the experimented tasks. For one complexly structured task, SAM and UNet with pseudo labels, trained using SAM's masks, fail to produce accurate results. It is notable that some of the tasks have small training dataset sizes, which limits the test accuracy of UNet. The results are in accordance with recent literature which shows that zero-shot SAM can have comparable performance to state-of-the-art models with large and distinct objects, but when it comes to small, complex structures, SAM is not up to par accuracy-wise to the state-of-the-art medical segmentation models.