SDSL: Spectral Distance Scaling Loss Pretraining SwinUNETR for 3D Medical Image Segmentation

Recent approaches utilizing self-supervised learning with masked image modeling (MIM) have demonstrated great performance. However, applying MIM naively to small datasets results in poor generalization to downstream tasks. We hypothesize that capturing detailed anatomical structures can compensate for the limitations posed by the dataset shortage; thus, we introduce Spectral Distance Scaling Loss (SDSL), designed to improve generalization in medical imaging tasks. Unlike traditional pixel-based methods, SDSL incorporates frequency-domain information to enhance encoder representations, ensuring balanced learning of both low and high-frequency details. Furthermore, wavelet multi resolution decomposition was utilized to enable the pretrained model to reconstruct frequency information across multiple stages. The comprehensive experiments demonstrate that SDSL pretraining yields sharper reconstruction results and more accurate segmentation outcomes than existing methods. The proposed approach achieved the highest average Dice scores of 84.17% on the Beyond the Cranial Vault dataset, 98.20% on the Medical Segmentation Decathlon Spleen dataset, and 90.38% on the Multimodality Whole Heart Segmentation dataset. The findings highlight the potential of SDSL in advancing medical imaging techniques by effectively handling spectral variations and improving model generalization.