USPoint: Self-Supervised Interest Point Detection and Description for Ultrasound-Probe Motion Estimation During Fine-Adjustment Standard Fetal Plane Finding.

ABSTRACT: Ultrasound (US)-probe motion estimation is a fundamental problem in automated standard plane locating during obstetric US diagnosis. Most recent existing recent works employ deep neural network (DNN) to regress the probe motion. However, these deep regressionbased methods leverage the DNN to overfit on the specific training data, which is naturally lack of generalization ability for the clinical application. In this paper, we are back to generalized US feature learning rather than deep parameter regression. We propose a self-supervised learned local detector and descriptor, named USPoint, for US-probe motion estimation during the fine-adjustment phase of fetal plane acquisition. Specifically, a hybrid neural architecture is designed to simultaneously extract a local feature, and further es

SUBMITTER: Zhao C 

PROVIDER: S-EPMC7614558 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature