UNASSIGNED: The study used 50 slices from different DBT volumes for evaluation. The AMS-U-Net approach consisted of four stages: image pre-processing, AMS-U-Net training, image segmentation, and post-processing. The model performance was evaluated by calculating the true positive ratio (TPR), false positive ratio (FPR), F-score, intersection over union (IoU), and 95% Hausdorff distance (pixels) as they are appropriate for datasets with class imbalance.
UNASSIGNED: The model achieved 0.911, 0.003, 0.911, 0.900, 5.82 for TPR, FPR, F-score, IoU, and 95% Hausdorff distance, respectively.
UNASSIGNED: The AMS-U-Net model demonstrated impressive visual and quantitative results, achieving high accuracy in mass segmentation without the need for human interaction. This capability has the potential to significantly increase clinical efficiency and workflow in DBT for breast cancer screening.
■该研究使用来自不同DBT体积的50个切片进行评估。AMS-U-Net方法包括四个阶段:图像预处理,AMS-U-Net训练,图像分割,和后处理。通过计算真正比(TPR)评估模型性能,假阳性率(FPR),F分数,联合相交(IoU),和95%Hausdorff距离(像素),因为它们适用于具有类不平衡的数据集。
■该模型实现了TPR的0.911、0.003、0.911、0.900、5.82,FPR,F分数,IoU,和95%的Hausdorff距离,分别。
■AMS-U-Net模型展示了令人印象深刻的视觉和定量结果,在质量分割中实现高精度,而不需要人机交互。这种能力有可能显著提高DBT用于乳腺癌筛查的临床效率和工作流程。