基于注意机制的卷积神经网络自动心脏分割

Guodong Zhang, Yu Liu, Wei Guo, Wenjun Tan, Zhaoxuan Gong, M. Farooq
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引用次数: 0

摘要

由于CT图像中心脏图像对比度较差,因此心脏分割具有挑战性。由于人工心脏分割繁琐且耗时,我们提出了一种基于注意力的卷积神经网络(CNN)进行心脏分割。首先,对多组织CT图像进行一热预处理。然后应用带注意门的U-Net网络获取心脏区域。我们将我们的方法与几种CNN方法在骰子系数方面进行了比较。结果表明,该方法优于其他分割方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic heart segmentation based on convolutional networks using attention mechanism
Heart segmentation is challenging due to the poor image contrast of heart in the CT images. Since manual segmentation of the heart is tedious and time-consuming, we propose an attention-based Convolution Neural Network (CNN) for heart segmentation. First, one-hot preprocessing is performed on the multi-tissue CT images. U-Net network with Attention-gate is then applied to obtain the heart region. We compared our method with several CNN methods in terms of dice coefficient. Results show that our method outperforms other methods for segmentation.
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