Sam2Rad architecture The Sam2Rad architecture incorporates a (hierarchical) two-way attention module to predict prompts for queried objects. Each object/class is represented by learnable queries . The Prompt Predictor Network (PPN) predicts bounding box coordinates of the target object , an intermediate mask prompt , and high-dimensional prompt embeddings . The prompt embeddings can represent various prompts suitable for the task, such as several point prompts or high-level semantic information. The predicted prompts (i.e., , , & ) are then fed to SAM’s mask decoder to generate the final segmentation mask. PPN also supports multi-class medical image segmentation by using class-specific queries .

Sam2Rad Explained: Teaching SAM2 to Prompt Itself on Ultrasound

2 Comments / Machine Learning, Image Segmentation /

AI FOR MEDICAL IMAGING AND HEALTHCARE · 15 MIN READ · Analysis by the aitrendblend editorial team· Sam2Rad Segment Anything Model SAM2 ultrasound prompt learning musculoskeletal imaging zero shot segmentation A bone outline traced automatically on an ultrasound frame. Image styling is illustrative of the pipeline described in the paper. Give the Segment Anything Model […]

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