Light-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound
Accurate and consistent predictions of echocardiography parameters are important for cardiovascular diagnosis and treatment. In particular, segmentations of the left ventricle can be used to derive ventricular volume, ejection fraction (EF) and other relevant measurements. In this paper we propose a...
Saved in:
Main Authors | , , |
---|---|
Format | Journal Article |
Language | English |
Published |
06.07.2022
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Accurate and consistent predictions of echocardiography parameters are
important for cardiovascular diagnosis and treatment. In particular,
segmentations of the left ventricle can be used to derive ventricular volume,
ejection fraction (EF) and other relevant measurements. In this paper we
propose a new automated method called EchoGraphs for predicting ejection
fraction and segmenting the left ventricle by detecting anatomical keypoints.
Models for direct coordinate regression based on Graph Convolutional Networks
(GCNs) are used to detect the keypoints. GCNs can learn to represent the
cardiac shape based on local appearance of each keypoint, as well as global
spatial and temporal structures of all keypoints combined. We evaluate our
EchoGraphs model on the EchoNet benchmark dataset. Compared to semantic
segmentation, GCNs show accurate segmentation and improvements in robustness
and inference runtime. EF is computed simultaneously to segmentations and our
method also obtains state-of-the-art ejection fraction estimation. Source code
is available online: https://github.com/guybenyosef/EchoGraphs. |
---|---|
DOI: | 10.48550/arxiv.2207.02549 |