Enhancing a 3D Foundation Model with Gaussian Sampling for Interactive Biomedical Image Segmentation

Interactive segmentation of 3D medical images seeks to produce accurate object masks with minimal user input, substantially alleviating the burden of manual annotation. For the CVPR 2025 Foundation Models for Interactive 3D Biomedical Image Segmentation Challenge, we extend the VISTA3D foundation model—a state-of-the-art 3D segmentation network supporting both automatic and interactive modes—by introducing several targeted improvements for robust interactive segmentation. First, we propose a Gaussian Edge-Center point sampling strategy, which leverages Gaussian-weighted randomness combined with center/edge distance transforms to preferentially sample points at object centers and boundaries. This yields more realistic and effective foreground/background click simulations during training. Second, we integrate this sampler into a two-stage fine-tuning pipeline: initial conventional fine-tuning with provided pre-trained weights, followed by prompt-focused fine-tuning using our improved sampling strategy. Third, to meet the challenge’s 90-second runtime limit, we optimize inference by dynamically adjusting the region of interest (ROI) size and resolution based on input voxel spacing, including adaptive downsampling and ROI cropping. We trained models for both tracks—using 4×A100 GPUs for the full dataset and 4×A800 GPUs for the 10 https://github.com/M4cheal/GS_MedSegFM .