@Article{cmes.2026.087647,
AUTHOR = {Weiqing Liu, Bin Li, Lianfang Tian, Qianhui Qiu},
TITLE = {Scale Ladder Consistency for Structure-Aware Multimodal Representation Learning in 3D Medical Image Segmentation},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {148},
YEAR = {2026},
NUMBER = {2},
PAGES = {0--0},
URL = {http://www.techscience.com/CMES/v148n2/68599},
ISSN = {1526-1506},
ABSTRACT = {Self-supervised representation learning can reduce the dependence of three-dimensional (3D) medical image segmentation on dense voxel annotations. In multimodal 3D medical imaging, intensity-reconstruction pre-training provides dense appearance supervision but does not explicitly distinguish the structural regions that determine segmentation boundaries and small targets. A second mismatch arises in scale learning: encoder-decoder networks provide multi-scale feature maps, but they do not explicitly supervise how fine anatomical structures weaken or persist across neighboring scales. To address these mismatches, this study proposes Scale Ladder Consistency (SLC), a structure-aware self-supervised representation learning framework for multimodal 3D medical image segmentation. SLC combines Scale-Space Structural Reconstruction (SSR), Hybrid Mask, and Scale Ladder (SL) in its structural pre-training path. SSR replaces intensity recovery with structure prediction, Hybrid Mask increases supervision on fine structural regions, and SL learns neighboring-scale structural transitions through bidirectional prediction. Subset-to-Full Regularization (S2F) further stabilizes case-level representations during pre-training. Experimental results on the Brain Tumor Segmentation 2019 (BraTS19) and carotid artery datasets demonstrate that SLC consistently outperforms matched scratch fine-tuning and achieves competitive performance against recent segmentation and self-supervised methods. These results indicate that structure-aware and cross-scale self-supervised objectives can provide effective representations for multimodal 3D medical segmentation.},
DOI = {10.32604/cmes.2026.087647}
}