Automatic and accurate medical image segmentation remains a fundamental task in computer-aided diagnosis and treatment planning. Recent advances in foundation models, such as the medical-focused Segment Anything Model (MedSAM), have demonstrated strong performance but face challenges in many medical applications due to anatomical complexity and a limited domain-specific prompt. This work introduces a methodology that enhances segmentation robustness and precision by automatically generating multiple informative point prompts, rather than relying on single inputs. The proposed approach randomly samples sets of spatially distributed point prompts based on image features, enabling MedSAM to better capture fine-grained anatomical structures and boundaries. During inference, probability maps are aggregated to reduce local misclassifications without additional model training. Extensive experiments on various computed tomography (CT) and magnetic resonance imaging (MRI) datasets demonstrate improvements in Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD) metrics compared to baseline SAM and Scribble Prompt models. A semi-automatic point sampling version based on the ground truth segmentations yielded enhanced results, achieving up to 92.1% DSC and 86.6% NSD, with significant gains in delineating complex organs such as the pancreas, colon, kidney, and brain tumours. The main novelty of our method consists of effectively combining the results of multiple point prompts into the medical segmentation pipeline so that single-point prompt methods are outperformed. Overall, the proposed model offers a straightforward yet effective approach to improve medical image segmentation performance while maintaining computational efficiency.
Nazzal et al. (Thu,) studied this question.