To address the issues of poor accuracy caused by complex structures and noise interference in hippocampus MRI segmentation, an improved nnU-Net segmentation algorithm is proposed. First, the introduction of Self-Calibrated Convolutions enhances the ability to capture irregular targets and edge details. Second, an adaptive threshold-constrained dynamic channel attention mechanism is proposed to optimize the allocation of channel weights, strengthening the features of the target region while effectively suppressing noise interference. Experimental results show that the proposed algorithm significantly outperforms nnU-Net in terms of Dice coefficient, IoU, and sensitivity. On the MSD Hippocampus and LPBA40 datasets, the DSC improved by 1.72% and 4.52%, the IoU improved by 2.89% and 5.4%, and the recall rate improved by 1.55% and 4.21%, respectively. Further visualization analysis of the segmentation results confirms that the proposed algorithm demonstrates excellent performance in segmenting complex structures such as hippocampal boundaries, vascular bifurcations, and lung infection regions.