Automated Placental ROI Selection for Quantitative Ultrasound Analysis Using Boundary-Aware Thresholding

Quantitative ultrasound (QUS) analysis of placental tissue holds significant promise for evaluating pregnancy health and detecting placental pathologies. However, widespread clinical adoption of QUS is limited by the need for manual selection of a proper region of interest (ROI) for the calculation, which increases the analysis time and introduces operator variability. This study presents an automated ROI determination method that uses deep learning segmentation directly on minimally processed raw radio frequency (RF) data (along with corresponding B-mode data for comparison) to replace manual ROI selection in placental QUS analysis while also taking advantage of the model's internal confidence thresholds to reduce inclusion of nonplacental tissue near ambiguous tissue boundaries. Using a diverse, multinational dataset of 1084 annotated ultrasound images, the proposed automated ROI model achieved a mean dice similarity coefficient (DSC) above 0.8 for confidence thresholds between 50% and 75%. At thresholds above 75%, precision improved, but both DSC and recall performance declined, and the amount of placental tissue identified for an accurate QUS calculation fell below the minimum required ROI size. Bland-Altman analysis showed tight agreement between the QUS values derived from ground-truth ROIs and automated ROIs up to a 75% confidence threshold, but reduced agreement at higher thresholds consistent with reducing the placental pixel area for below the minimum. These findings demonstrate both the feasibility of automated ROI determination and the need for careful consideration of the confidence thresholds when assessing model performance.