Deep learning-based plane pose regression in obstetric ultrasound.

ABSTRACT:

Purpose

In obstetric ultrasound (US) scanning, the learner's ability to mentally build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US image represents a major challenge in skill acquisition. We aim to build a US plane localisation system for 3D visualisation, training, and guidance without integrating additional sensors.

Methods

We propose a regression convolutional neural network (CNN) using image features to estimate the six-dimensional pose of arbitrarily oriented US planes relative to the fetal brain centre. The network was trained on synthetic images acquired from phantom 3D US volumes and fine-tuned on real scans. Training data was generated by slicing US volumes into imaging planes in Unity at random coordinates and more densely around the st

SUBMITTER: Di Vece C 

PROVIDER: S-EPMC9110476 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature