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.

翻译：新发布的分段任意模型（Segment Anything Model, SAM）因其卓越的分割精度、多样的输入提示、训练能力以及高效模型设计，成为图像处理中的热门工具。然而，当前模型在多样化数据集上训练，并未针对医学图像（尤其是超声图像）进行优化。超声图像噪声较大，难以分割出重要结构。在本项目中，我们开发了ClickSAM，通过点击提示对分段任意模型进行微调，以适用于超声图像。ClickSAM包含两个训练阶段：第一阶段基于真实轮廓中心点的单一点击提示进行训练；第二阶段通过额外添加正向和负向点击提示来提升模型性能。通过将第一阶段预测结果与真实掩膜对比，计算真阳性、假阳性及假阴性分割区域。基于真阳性和假阴性区域生成正向点击，基于假阳性区域生成负向点击。随后采用质心泰森多边形图（Centroidal Voronoi Tessellation）算法在每个分割区域内采集正向和负向点击提示，以在第二阶段训练中优化模型性能。通过点击训练方法，ClickSAM在超声图像分割任务中展现出优于现有模型的性能。