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“分割一切模型”（Segment Anything Model, SAM）因其卓越的分割精度、多样的输入提示、训练能力及高效的模型设计而成为图像处理领域的热门工具。然而，其当前模型基于多样化数据集进行训练，并未针对医学图像（尤其是超声图像）进行特化。超声图像往往包含大量噪声，使得关键结构的分割变得困难。在本项目中，我们开发了ClickSAM，该方法通过点击提示对SAM进行微调以用于超声图像分割。ClickSAM包含两个训练阶段：第一阶段基于位于真实轮廓中心的单一点击提示进行训练；第二阶段则通过添加正负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码进行对比，计算真阳性、假阳性和假阴性分割区域。其中，阳性点击由真阳性和假阴性区域生成，阴性点击由假阳性区域生成。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收集正负点击提示，用于第二阶段训练中增强模型性能。通过这种点击训练方法，ClickSAM在超声图像分割任务中展现出优于现有模型的性能。