Model-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos.

ABSTRACT: The application of segmentation methods to medical imaging has the potential to create novel diagnostic support models. With respect to fetal ultrasound, the thoracic wall is a key structure on the assessment of the chest region for examiners to recognize the relative orientation and size of structures inside the thorax, which are critical components in neonatal prognosis. In this study, to improve the segmentation performance of the thoracic wall in fetal ultrasound videos, we proposed a novel model-agnostic method using deep learning techniques: the Multi-Frame + Cylinder method (MFCY). The Multi-frame method (MF) uses time-series information of ultrasound videos, and the Cylinder method (CY) utilizes the shape of the thoracic wall. To evaluate the achieved improvement, we performed segm

SUBMITTER: Shozu K 

PROVIDER: S-EPMC7766150 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature