豪斯多夫距离
分割
人工智能
Sørensen–骰子系数
卷积神经网络
计算机科学
心室
图像分割
模式识别(心理学)
计算机视觉
尺度空间分割
医学
心脏病学
作者
Yifeng Tan,Lina Yang,Xichun Li,Zhaozong Meng
标识
DOI:10.1109/csci54926.2021.00322
摘要
Cardiac MRI image segmentation is of great importance for evaluating cardiac function and diagnosing diseases. Manual segmentation is time-consuming and tedious, so automatic segmentation is very popular in practical applications. In this paper, we propose an improved full convolutional neural network based on 2D-Unet for automatic segmentation of the left ventricle, right ventricle and myocardium. Experiments were conducted on the ACDC 2017 Challenge Training dataset. The segmentation results were assessed by means of average vertical distance, Dice coefficient and Hausdorff distance. Our model reduces the amount of parameters, improves the training speed, uses the fusion loss function, and maintains a satisfactory segmentation accuracy of left ventricle, right ventricle and myocardium.
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