  The newly released Segment Anything Model (SAM) is a popular tool used in
image processing due to its superior segmentation accuracy, variety of input
prompts, training capabilities, and efficient model design. However, its
current model is trained on a diverse dataset not tailored to medical images,
particularly ultrasound images. Ultrasound images tend to have a lot of noise,
making it difficult to segment out important structures. In this project, we
developed ClickSAM, which fine-tunes the Segment Anything Model using click
prompts for ultrasound images. ClickSAM has two stages of training: the first
stage is trained on single-click prompts centered in the ground-truth contours,
and the second stage focuses on improving the model performance through
additional positive and negative click prompts. By comparing the first stage
predictions to the ground-truth masks, true positive, false positive, and false
negative segments are calculated. Positive clicks are generated using the true
positive and false negative segments, and negative clicks are generated using
the false positive segments. The Centroidal Voronoi Tessellation algorithm is
then employed to collect positive and negative click prompts in each segment
that are used to enhance the model performance during the second stage of
training. With click-train methods, ClickSAM exhibits superior performance
compared to other existing models for ultrasound image segmentation.
