![]() ![]() However, a significant limitation of many current medical image segmentation models is their task-specific nature. ![]() Semi- or fully automatic segmentation methods can significantly reduce the time and labor required, increase consistency, and enable the analysis of large-scale datasets 4.ĭeep learning-based models have shown great promise in medical image segmentation due to their ability to learn intricate image features and deliver accurate segmentation results across a diverse range of tasks, from segmenting specific anatomical structures to identifying pathological regions 5. Manual segmentation has long been the gold standard for delineating anatomical structures and pathological regions, but this process is time-consuming, labor-intensive, and often requires a high degree of expertise. Accurate segmentation is essential for many clinical applications, including disease diagnosis, treatment planning, and monitoring of disease progression 2, 3. Segmentation is a fundamental task in medical imaging analysis, which involves identifying and delineating regions of interest (ROI) in various medical images, such as organs, lesions, and tissues 1. By delivering accurate and efficient segmentation across a wide spectrum of tasks, MedSAM holds significant potential to expedite the evolution of diagnostic tools and the personalization of treatment plans. We conduct a comprehensive evaluation on 86 internal validation tasks and 60 external validation tasks, demonstrating better accuracy and robustness than modality-wise specialist models. The model is developed on a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. Here we present MedSAM, a foundation model designed for bridging this gap by enabling universal medical image segmentation. However, existing methods, often tailored to specific modalities or disease types, lack generalizability across the diverse spectrum of medical image segmentation tasks. Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. ![]()
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