Name
Abstract | ||
|---|---|---|
The assessment of right ventricular (RV) function is essential in the diagnosis of many cardiac diseases. Magnetic resonance imaging (MRI) offers an excellent solution to image right ventricle non-invasively with high contrast and temporal resolution. Manual assessment of the RV function from MRI sequences is tedious and time-consuming and automating the process is of great interest. This study proposes a convolutional neural network-based machine learning approach to automate the delineation of the RV from a sequence of MRI. The architecture of the neural network differs from that of a widely-known U-Net approach. Additionally, the proposed approach used image concatenation to create and utilize 3D spatial information in the segmentation process. Quantitative evaluations were performed over 256 images acquired from 16 patients in publicly available data in comparison to manual delineations. Comparisons with the results by U-Net demonstrated that the proposed method outperforms the prior state-of-the-art method. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8857626 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
Right ventricular segmentation, magnetic resonance imaging, convolutional neural networks, machine learning, cardiac imaging | Spatial analysis,Computer vision,Convolutional neural network,Segmentation,Computer science,Image segmentation,Artificial intelligence,Concatenation,Artificial neural network,Temporal resolution,Magnetic resonance imaging | Conference |
Volume | ISSN | Citations |
2019 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hakimeh Purmehdi | 1 | 0 | 0.34 |
| Abhilash Rakkunedeth Hareendranathan | 2 | 0 | 2.70 |
| Noga, M. | 3 | 10 | 7.74 |
| Kumaradevan Punithakumar | 4 | 216 | 24.40 |