AUSAM: Adaptive Unified Segmentation Anything Model for multi-modality tumor segmentation and enhanced detection in medical imaging

Abstract

Tumor segmentation in medical imaging is critical for diagnosis, treatment planning, and prognosis, yet remains challenging due to limited annotated data, tumor heterogeneity, and modality-specific complexities in CT, MRI, and histopathology. Although the Segment Anything Model (SAM) shows promise as a zero-shot learner, it struggles with irregular tumor boundaries and domain-specific variations. We introduce the Adaptive Unified Segmentation Anything Model (AUSAM). This novel framework extends SAM’s capabilities for multi-modal tumor segmentation by integrating an intelligent prompt module, dynamic sampling, and stage-based thresholding. Specifically, clustering-based prompt learning (DBSCAN for CT/MRI and K-means for histopathology) adaptively allocates prompts to capture challenging tumor regions, while entropy-guided sampling and dynamic thresholding systematically reduce annotation requirements and computational overhead. Validated on diverse benchmarks—LiTS (CT), FLARE 2023 (CT/MRI), ORCA, and OCDC (histopathology)—AUSAM achieves state-of-the-art Dice Similarity Coefficients (DSC) of 94.25%, 91.84%, 87.59%, and 91.84%, respectively, with significantly reduced data usage. As the first framework to adapt SAM for multi-modal tumor segmentation, AUSAM sets a new standard for precision, scalability, and efficiency. It is offered in two variants: AUSAM-Lite for resource-constrained environments and AUSAM-Max for maximum segmentation accuracy, thereby advancing medical imaging and clinical decision-making.