Name
Abstract | ||
|---|---|---|
This work presents a novel deep learning architecture called BNU-Net for the purpose of cardiac segmentation based on short-axis MRI images. Its name is derived from the Batch Normalized (BN) U-Net architecture for medical image segmentation. New generations of deep neural networks (NN) are called convolutional NN (CNN). CNNs like U-Net have been widely used for image classification tasks. CNNs are supervised training models which are trained to learn hierarchies of features automatically and robustly perform classification. Our architecture consists of an encoding path for feature extraction and a decoding path that enables precise localization. We compare this approach with a parallel approach named U-Net. Both BNU-Net and U-Net are cardiac segmentation approaches: while BNU-Net employs batch normalization to the results of each convolutional layer and applies an exponential linear unit (ELU) approach that operates as activation function, U-Net does not apply batch normalization and is based on Rectified Linear Units (ReLU). The presented work (i) facilitates various image preprocessing techniques, which includes affine transformations and elastic deformations, and (ii) segments the preprocessed images using the new deep learning architecture. We evaluate our approach on a dataset containing 805 MRI images from 45 patients. The experimental results reveal that our approach accomplishes comparable or better performance than other state-of-the-art approaches in terms of the Dice coefficient and the average perpendicular distance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BIBE.2019.00137 | 2019 IEEE 19TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE) |
Keywords | Field | DocType |
Magnetic Resonance Imaging, Batch Normalization, Exponential Linear Units | Affine transformation,Rectifier (neural networks),Normalization (statistics),Pattern recognition,Computer science,Segmentation,Feature extraction,Image segmentation,Artificial intelligence,Deep learning,Contextual image classification,Machine learning | Conference |
ISSN | Citations | PageRank |
2471-7819 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenhui Chu | 1 | 0 | 0.34 |
| Giovanni Molina | 2 | 0 | 0.68 |
| Nikhil V. Navkar | 3 | 0 | 0.34 |
| Christoph F. Eick | 4 | 0 | 0.34 |
| Aaron Becker | 5 | 118 | 24.52 |
| Panagiotis Tsiamyrtzis | 6 | 269 | 33.31 |
| Nikolaos V. Tsekos | 7 | 93 | 19.91 |