Automated Field of Interest Determination for Quantitative Ultrasound Analyses of Cervical Tissues: Toward Real-time Clinical Translation in Spontaneous Preterm Birth Risk Assessment

Objective: Quantitative ultrasound (QUS) analysis of the human cervix is valuable for predicting spontaneous preterm birth risk. However, this approach currently requires an offline processing step wherein a medically trained analyst manually draws a free-hand field of interest (Manual FOI) for QUS computation. This offline step hinders the clinical adoption of QUS. To address this challenge, we developed a method to determine automatically the cervical FOI (Auto FOI). This study's objective is to evaluate the agreement between QUS results obtained from the Auto and Manual FOIs and assess the feasibility of using the Auto FOI to replace the Manual FOI for cervical QUS computation.

Methods: The auto FOI method was developed and evaluated using cervical ultrasound data from 527 pregnant women, using Manual FOIs as the reference. A deep learning model was developed using the cervical B-mode image as the input to determine automatically the FOI.

Results: Quantitative comparison between the Auto and Manual FOIs yielded a high pixel accuracy of 97% and a Dice coefficient of 87%. Further, the Auto FOI yielded QUS biomarker values that were highly correlated with those obtained from the Manual FOIs. For example, the Pearson correlation coefficient was 0.87 between attenuation coefficient values obtained using Auto and Manual FOIs. Further, Bland-Altman analyses showed negligible bias between QUS biomarker values computed using the Auto and Manual FOIs.

Conclusion: The results support the feasibility of using Auto FOIs to replace Manual FOIs in QUS computation, an important step toward the clinical adoption of QUS technology.