Computerized Self-Assessment of Automated Lesion Segmentation in Breast Ultrasound: Implication for CADx Applied to Findings in the Axilla

摘要

We developed a self-assessment method in which the CADx system provided a confidence level for its lesion segmentations. The self-assessment was performed by a fuzzy-inference system based on 4 computer-extracted features of the computer-segmented lesions in a leave-one-case-out evaluation protocol. In instances where the initial segmentation received a low assessment rating, lesions were re-segmented using the same segmentation method but based on a user-defined region-of-interest. A total of 542 cases with 1133 lesions were collected in this study, and we focused here on the 97 normal lymph nodes in this dataset since these pose challenges for automated segmentation due to their inhomogeneous appearance. The percentage of all lesions with satisfactory segmentation (i.e., normalized overlap with the radiologist-delineated lesion >=0.3) was 85%. For normal lymph nodes, however, this percentage was only 36%. Of the lymph nodes, 53 received a low confidence rating (<0.3) for their initial segmentation. When those lymph nodes were re-segmented, the percentage with a satisfactory segmentation improved to 80.0%. Computer-assessed confidence levels demonstrated potential to 1) help radiologists decide whether to use or disregard CADx output, and 2) provide a guide for improvement of lesion segmentation.