B-MIM: Biased Masked Image Modeling for Generalizable Segmentation of Fine-Grained Anatomical Structures

TL;DR - B-MIM is a self-supervised pretraining method that reduces global semantic alignment to make 3D CT encoders more sensitive to fine anatomical details. It improves cross-dataset segmentation of intricate structures while requiring only partial parameter updates during downstream training.

• Modifies the iBOT objective to prioritize local patch reconstruction and high-frequency morphology.
• Pretrains a 3D Swin Transformer on 9,955 abdominal CT studies curated from 17 public sources.
• Improves topological fidelity (clDice) for liver vessel segmentation across datasets.
• Achieves competitive tumor-segmentation Dice scores versus fully fine-tuned baselines while updating fewer parameters.