Name
Title | ||
|---|---|---|
Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks. |
Abstract | ||
|---|---|---|
Accurate acquisition of fetal ultrasound (US) standard planes is one of the most crucial steps in obstetric diagnosis. The conventional way of standard plane acquisition requires a thorough knowledge of fetal anatomy and intensive manual labors. Hence, automatic approaches are highly demanded in clinical practice. However, automatic detection of standard planes containing key anatomical structures from US videos remains a challenging problem due to the high intra-class variations of standard planes. Unlike previous studies that developed specific methods for different anatomical standard planes respectively, we present a general framework to detect standard planes from US videos automatically. Instead of utilizing hand-crafted visual features, our framework explores spatio-temporal feature learning with a novel knowledge transferred recurrent neural network (T-RNN), which incorporates a deep hierarchical visual feature extractor and a temporal sequence learning model. In order to extract visual features effectively, we propose a joint learning framework with knowledge transfer across multi-tasks to address the insufficiency issue of limited training data. Extensive experiments on different US standard planes with hundreds of videos corroborate that our method can achieve promising results, which outperform state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24553-9_62 | MICCAI |
Keywords | Field | DocType |
LOCALIZATION | Training set,Computer vision,Pattern recognition,Convolutional neural network,Computer science,Knowledge transfer,Recurrent neural network,Extractor,Artificial intelligence,Anatomical structures,Sequence learning,Feature learning | Conference |
Volume | ISSN | Citations |
9349 | 0302-9743 | 33 |
PageRank | References | Authors |
1.90 | 6 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Chen | 1 | 1060 | 58.15 |
| Qi Dou | 2 | 837 | 57.52 |
| Dong Ni | 3 | 367 | 37.37 |
| Jie-Zhi Cheng | 4 | 59 | 4.49 |
| Jing Qin | 5 | 1109 | 95.43 |
| Shengli Li | 6 | 184 | 18.06 |
| Pheng-Ann Heng | 7 | 3565 | 280.98 |