PDF(Pubmed)
Abstract：
UNASSIGNED: The objective of this study was to develop a fully automatic mass segmentation method called AMS-U-Net for digital breast tomosynthesis (DBT), a popular breast cancer screening imaging modality. The aim was to address the challenges posed by the increasing number of slices in DBT, which leads to higher mass contouring workload and decreased treatment efficiency.
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.
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.
摘要：
■这项研究的目的是开发一种称为AMS-U-Net的全自动质量分割方法，用于数字乳房断层合成（DBT），一种流行的乳腺癌筛查成像方式。目的是解决DBT中切片数量不断增加所带来的挑战，这导致较高的质量轮廓工作量和降低的治疗效率。
■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。
■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。
