AlphaClick: Scaling Interactive Medical Image Segmentation at Test-time via Monte Carlo Tree Search

Powered by large-scale training data, promptable segmentation models such as the Segment Anything Model (SAM) have shown impressive generalization in medical image segmentation, where a few corrective clicks can turn a coarse mask into an accurate one. However, these clicks still have to be provided by experts, which is costly and limits the scalability of interactive segmentation in clinical practice. Existing automatic prompting methods, built on heuristics or reinforcement learning (RL) policies, decide clicks without looking ahead and cannot turn additional test-time computation into better segmentation. In this paper, we propose AlphaClick, a novel RL framework that scales interactive medical image segmentation at test time through Monte Carlo tree search (MCTS). Specifically, AlphaClick formulates corrective clicking as a sequential decision process and trains a policy-value network with the Dice improvement of each click as the reward. The policy proposes promising clicks, and the value predicts the final segmentation quality reachable from the current state. At inference, MCTS uses the policy to expand candidate clicks and the value to evaluate simulated click sequences, so that more simulations yield better click decisions without any ground truth. Extensive experiments on 80 public datasets covering seven imaging modalities show that AlphaClick outperforms existing automatic prompting methods by a large margin, reaching a Dice of 0.69 after ten corrective clicks compared with 0.43 for the strongest baseline, and that its accuracy improves steadily as the search grows from one to sixteen simulations per click. Results on three segmentation backbones and twelve unseen datasets further confirm its generality. In conclusion, AlphaClick makes test-time computation a new dimension for improving interactive segmentation and provides a promising solution for reducing the annotation burden in medical imaging. The code will be made publicly available.