Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view.

ABSTRACT: Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation. In this work, we address these three challenges with a multi-task learning approach that combines the classification of placental location (e.g., anterior, posterior) and semantic placenta segmentation in a single convolutional neural network. Through the classification task the model can learn from larger and more diverse datasets while improving the accuracy of the segmentation task in particular in limited training set conditions. With this approach we investigate the variab

SUBMITTER: Zimmer VA 

PROVIDER: S-EPMC7614009 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature