BrainIAC: Interactive 3D Brain Lesion Segmentation across Heterogeneous MRI Modalities with Online Adaptation

arXiv:2609.23026 · cs.CV · Submitted 2026-09-19 · Read on arXiv

cs.CV

Submitted: 2026-09-19

Updated: 2026-09-19

Code: https://github.com/WenTXuL/BrainIAC

Terminology

• Adaptation — 181× in this paper · explained in 1 episode
• Online Adaptation — 102× in this paper · explained in 3 episodes
• Foundation Model — 10× in this paper · explained in 3 episodes
• AGN — 8× in this paper · explained in 1 episode
• Distribution Shift — 7× in this paper · explained in 5 episodes
• Foundation Models — 7× in this paper · explained in 7 episodes
• Click-Centered Gaussian — 5× in this paper
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Unified Framework — 5× in this paper · explained in 5 episodes
• nnU-Net — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Ablation study — 4× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Continual Learning — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Segment Anything Model — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• International Conference on Learning Representations — 1× in this paper
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Segment Anything Model (SAM) — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes

Sources

• nnInteractive: Redefining 3D Promptable Segmentation
• MedSAM2: Segment Anything in 3D Medical Images and Videos
• Interactive segmentation of medical images through fully convolutional neural networks

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