BNU-Net: A Novel Deep Learning Approach for LV MRI Analysis in Short-Axis MRI
BNU-Net: A Novel Deep Learning Approach for LV MRI Analysis in Short-Axis MRI
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DOI：
10.1109/bibe.2019.00137
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发表时间：
2019-10
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通讯作者：
Wenhui Chu;Giovanni Molina;N. Navkar;Christoph F. Eick;Aaron T. Becker;P. Tsiamyrtzis;N. Tsekos
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作者：
Wenhui Chu;Giovanni Molina;N. Navkar;Christoph F. Eick;Aaron T. Becker;P. Tsiamyrtzis;N. Tsekos
这项工作提出了一种称为BNU-Net的新型深度学习架构，用于基于短轴MRI图像的心脏分割。它的名字来源于用于医学图像分割的批量归一化（BN）U-Net架构。新一代的深度神经网络（NN）被称为卷积NN（CNN）。像U-Net这样的CNN已经被广泛用于图像分类任务。CNN是有监督的训练模型，经过训练可以自动学习特征的层次结构，并鲁棒地执行分类。我们的架构包括一个编码路径的特征提取和解码路径，使精确定位。我们将这种方法与一种名为U-Net的并行方法进行比较。BNU-Net和U-Net都是心脏分割方法：虽然BNU-Net对每个卷积层的结果进行批量归一化，并应用作为激活函数的指数线性单元（ELU）方法，但U-Net不应用批量归一化，而是基于整流线性单元（ReLU）。所提出的工作（i）促进了各种图像预处理技术，包括仿射变换和弹性变形，以及（ii）使用新的深度学习架构对预处理图像进行分割。我们评估了我们的方法，包含805个MRI图像从45例患者的数据集。实验结果表明，我们的方法完成可比或更好的性能比其他国家的最先进的方法在骰子系数和平均垂直距离。
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.