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arXiv · 2409.06821

Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts

Assefa Seyoum Wahd·Banafshe Felfeliyan·Yuyue Zhou·Shrimanti Ghosh·Adam McArthur·Jiechen Zhang·Jacob L. Jaremko·Abhilash Hareendranathan
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Abstract

Foundation models like the segment anything model require high-quality manual prompts for medical image segmentation, which is time-consuming and requires expertise. SAM and its variants often fail to segment structures in ultrasound (US) images due to domain shift. We propose Sam2Rad, a prompt learning approach to adapt SAM and its variants for US bone segmentation without human prompts. It introduces a prompt predictor network (PPN) with a cross-attention module to predict prompt embeddings from image encoder features. PPN outputs bounding box and mask prompts, and 256-dimensional embeddings for regions of interest. The framework allows optional manual prompting and can be trained end-to-end using parameter-efficient fine-tuning (PEFT). Sam2Rad was tested on 3 musculoskeletal US datasets: wrist (3822 images), rotator cuff (1605 images), and hip (4849 images). It improved performance across all datasets without manual prompts, increasing Dice scores by 2-7% for hip/wrist and up to 33% for shoulder data. Sam2Rad can be trained with as few as 10 labeled images and is compatible with any SAM architecture for automatic segmentation.

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Assefa Seyoum Wahd, Banafshe Felfeliyan, Yuyue Zhou, Shrimanti Ghosh, Adam McArthur, Jiechen Zhang, Jacob L. Jaremko, Abhilash Hareendranathan. 2024-09-10. Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts. https://doi.org/10.1016/j.compbiomed.2025.109725

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