Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization.

ABSTRACT: The placenta is crucial to fetal well-being and it plays a significant role in the pathogenesis of hypertensive pregnancy disorders. Moreover, a timely diagnosis of placenta previa may save lives. Ultrasound is the primary imaging modality in pregnancy, but high-quality imaging depends on the access to equipment and staff, which is not possible in all settings. Convolutional neural networks may help standardize the acquisition of images for fetal diagnostics. Our aim was to develop a deep learning based model for classification and segmentation of the placenta in ultrasound images. We trained a model based on manual annotations of 7,500 ultrasound images to identify and segment the placenta. The model's performance was compared to annotations made by 25 clinicians (experts, trainees, midwi

SUBMITTER: Andreasen LA 

PROVIDER: S-EPMC9908915 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature