PDF(Pubmed)
Abstract：
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\'s 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.
摘要：
新发布的SegmentAnythingModel（SAM）是图像处理中使用的一种流行工具，由于其优越的分割精度，各种输入提示，培训能力,高效的模型设计。然而,它当前的模型是在不适合医学图像的不同数据集上训练的，特别是超声图像。超声图像往往有很多噪声，使得难以分割出重要的结构。在这个项目中,我们开发了ClickSAM，它使用超声图像的单击提示来微调“段任意模型”。ClickSAM有两个训练阶段：第一个阶段是在以真实轮廓为中心的单击提示上进行训练，第二阶段的重点是通过额外的正面和负面点击提示来提高模型性能。通过将第一阶段的预测与地面真相面具进行比较，真积极，假阳性,并计算假阴性段。使用真阳性和假阴性段生成阳性点击，和否定点击使用假阳性段生成。然后采用中心Voronoi细分算法在每个段中收集用于在第二阶段训练期间增强模型性能的正面和负面点击提示。使用单击训练方法，与其他现有的超声图像分割模型相比，ClickSAM具有出色的性能。
