ASC-3DSAM: Adjacent-Slice Correspondence-guided SAM for 3D Medical Image Segmentation

The Segment Anything Model (SAM) has recently gained popularity in the filed of 3D medical segmentation for its remarkable generalization capability. However, most SAM-based 2D to 3D adaptation methods requires prompting each slice in a medical volume, which is label-consuming. How to effectively propagate the prompts on the single slice to all slices while ensuring the continuity of the entire volume boundaries is still an unresolved challenge. To address this, we propose Adjacent-Slice Correspondence-guided 3D SAM(ASC-3DSAM), a novel approach for 3D medical segmentation, which matches key points or features between adjacent slices to establish correspondences for neighboring slices, thereby propagating prompt information to the entire volume using just a single prompted slice. Specifically, we present a Slice-Correlation Module (SCM), the adjacent slice as the query is segmented using the mask information from the central slice, thereby propagating prompt information to the entire volume. In addition, a hybrid continuity loss called Continuous Boundary-aware Loss(CBL) is proposed, which takes the form of a distance metric on the space of contours and integrates gradient information of the volume boundaries to supervise the continuity of the entire volume. Our approach is evaluated on two public datasets, including AMOS and BTCV. Experimental results demonstrate that ASC-3DSAM achieves state-of-the-art (SOTA) performance, with an improvement of approximately 1.86% of Dice and 1.92(mm) of HD95 compared to the next-best method. Furthermore, our method can produce smooth closed boundaries of the entire volumes outperforming the SOTA models.