AFTer-SAM: Adapting SAM with Axial Fusion Transformer for Medical Imaging Segmentation

The Segmentation Anything Model (SAM) has demonstrated effectiveness in various segmentation tasks. However, its application to 3D medical data has posed challenges due to its inherent design for both 2D and natural images. While there have been attempts to apply SAM to medical images on a slice-by-slice basis, the outcomes have been less than optimal. In this study, we introduce AFTer-SAM, an adaptation of SAM designed for volumetric medical image segmentation. By incorporating an Axial Fusion Transformer, AFTer-SAM is capable of capturing both intra-slice details and inter-slice contextual information, essential for accurate medical image segmentation. Given the potential computational challenges of training this enhanced model, we utilize Low-Rank Adaptation (LoRA) to efficiently finetune the weights of the Axial Fusion Transformer. This ensures a streamlined training process without compromising on performance. Our results indicate that AFTer-SAM offers significant improvements in volumetric medical image segmentation, suggesting a promising direction for the application of large pre-trained models in medical imaging.