Segmentation of four-chamber view images in fetal ultrasound exams using a novel deep learning model ensemble method

Fetal echocardiography, a specialized ultrasound application commonly utilized for fetal heart assessment, can greatly benefit from automated segmentation of anatomical structures, aiding operators in their evaluations. We introduce a novel approach that combines various deep learning models for segmenting key anatomical structures in 2D ultrasound images of the fetal heart. Our ensemble method combines the raw predictions from the selected models, obtaining the optimal set of segmentation components that closely approximate the distribution of the fetal heart, resulting in improved segmentation outcomes. The selection of these components involves sequential and hierarchical geometry filtering, focusing on the analysis of shape and relative distances. Unlike other ensemble strategies that average predictions, our method works as a shape selector, ensuring that the final segmentation aligns more accurately with anatomical expectations. Considering a large private dataset for model training and evaluation, we present both numerical and visual experiments highlighting the advantages of our method in comparison to the segmentations produced by the individual models and a conventional average ensemble. Furthermore, we show some applications where our method proves instrumental in obtaining reliable estimations.