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：最新发布的分割一切模型（Segment Anything Model, SAM）凭借其卓越的分割精度、多样的输入提示方式、可训练性以及高效的模型设计，已成为图像处理领域广泛应用的工具。然而，现有模型训练所依赖的多样化数据集并未针对医学图像（尤其是超声图像）进行专门优化。超声图像普遍存在噪声较强的特点，导致关键解剖结构难以准确分割。本研究开发的ClickSAM方法，通过引入点击提示机制对SAM模型进行微调，以适应超声图像分割任务。该模型采用两阶段训练策略：第一阶段以位于真实轮廓中心的单一点击提示进行训练，第二阶段则通过增加正负点击提示进一步提升模型性能。通过将第一阶段预测结果与真实掩膜进行比较，可分别计算真阳性、假阳性和假阴性分割区域。其中，基于真阳性和假阴性区域生成阳性点击，依据假阳性区域生成阴性点击。随后采用质心沃罗诺伊图算法在每个分割区域中提取正负点击提示，用于第二阶段训练中的模型性能优化。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表现。