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.
Index Terms—Magnetic Resonance Imaging; Batch Normalization;
Exponential Linear Units
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MULTI-COMPONENT DEFORMABLE MODELS COUPLED WITH 2D-3D U-NET FOR AUTOMATED PROBABILISTIC SEGMENTATION OF CARDIAC WALLS AND BLOOD
The segmentation of the ventricular wall and the blood pool in cardiac magnetic resonance imaging (MRI) has been inves- tigated for decades, given its important role for delineation of cardiac functioning and diagnosis of heart diseases. One of the major challenges is that the inner epicardium boundary is not always visible in the image domain, due to the mix- ture of blood and muscle structures, especially at the end of contraction, or systole. To address it, we propose a novel ap- proach for the cardiac segmentation in the short-axis (SAX) MRI: coupled deep neural networks and deformable models. First, a 2D U-Net is adopted for each magnetic resonance (MR) slice, and a 3D U-Net refines the segmentation results along the temporal dimension. Then, we propose a multi- component deformable model to extract accurate contours for both endo- and epicardium with global and local constraints. Finally, a partial blood classification is explored to estimate the presence of boundary pixels near the trabeculae and solid wall, and to avoid moving the endocardium boundary inward. Quantitative evaluation demonstrates the high accuracy, ro- bustness, and efficiency of our approach for the slices ac- quired at different locations and different cardiac phases.
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- Award ID(s):
- 1747778
- NSF-PAR ID:
- 10105304
- Date Published:
- Journal Name:
- 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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