  Brain tumor segmentation presents a formidable challenge in the field of
Medical Image Segmentation. While deep-learning models have been useful, human
expert segmentation remains the most accurate method. The recently released
Segment Anything Model (SAM) has opened up the opportunity to apply foundation
models to this difficult task. However, SAM was primarily trained on diverse
natural images. This makes applying SAM to biomedical segmentation, such as
brain tumors with less defined boundaries, challenging. In this paper, we
enhanced SAM's mask decoder using transfer learning with the Decathlon brain
tumor dataset. We developed three methods to encapsulate the four-dimensional
data into three dimensions for SAM. An on-the-fly data augmentation approach
has been used with a combination of rotations and elastic deformations to
increase the size of the training dataset. Two key metrics: the Dice Similarity
Coefficient (DSC) and the Hausdorff Distance 95th Percentile (HD95), have been
applied to assess the performance of our segmentation models. These metrics
provided valuable insights into the quality of the segmentation results. In our
evaluation, we compared this improved model to two benchmarks: the pretrained
SAM and the widely used model, nnUNetv2. We find that the improved SAM shows
considerable improvement over the pretrained SAM, while nnUNetv2 outperformed
the improved SAM in terms of overall segmentation accuracy. Nevertheless, the
improved SAM demonstrated slightly more consistent results than nnUNetv2,
especially on challenging cases that can lead to larger Hausdorff distances. In
the future, more advanced techniques can be applied in order to further improve
the performance of SAM on brain tumor segmentation.
