The latest advancement in large foundational model, SAM2, has demonstrated significant potential in 3D medical image segmentation due to their capability to effectively segment video streams. However, its application in medical image segmentation presents challenges, requiring extensive training on medical images or high-quality prompts provided by experts to achieve optimal performance. To address the aforementioned limitations, we propose SAM2-SP, which adopts Low-Rank Adaption for parameter-efficient fine-tuning and introduces a novel dynamic self-prompting strategy that generates most confident prompt templates from voxel features, enabling SAM2 to achieve domain adaptation in medical image segmentation without reliance on expert-level prompts. Extensive experiments show that SAM2-SP achieves state-of-the-art performance on the public Synapse dataset and the private EDC dataset, and even outperforms the compared task-specific segmentation approaches, the vanilla SAM and other SAM-based approaches.

Fetal Magnetic Resonance Imaging (MRI) is challenged by fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion frequently occurs in the acquisition of spatially adjacent slices. Motion correction for each slice is thus critical for the reconstruction of 3D fetal brain MRI. In this paper, we propose a novel multi-task learning framework that adopts a coarse-to-fine strategy to jointly learn the pose estimation parameters for motion correction and tissue segmentation map of each slice in fetal MRI. Particularly, we design a regression-based segmentation loss as a deep supervision to learn anatomically more meaningful features for pose estimation and segmentation. In the coarse stage, a U-Net-like network learns the features shared for both tasks. In the refinement stage, to fully utilize the anatomical information, signed distance maps constructed from the coarse segmentation are introduced to guide the feature learning for both tasks. Finally, iterative incorporation of the signed distance maps further improves the performance of both regression and segmentation progressively. Experimental results of cross-validation across two different fetal datasets acquired with different scanners and imaging protocols demonstrate the effectiveness of the proposed method in reducing the pose estimation error and obtaining superior tissue segmentation results simultaneously, compared with state-of-the-art methods.