论文标题
深度学习卷积神经网络的比较，用于径向涡轮旋转回波图像中的肝分割
A Comparison of Deep Learning Convolution Neural Networks for Liver Segmentation in Radial Turbo Spin Echo Images
论文作者
论文摘要
运动射击2D径向涡轮旋转回声（RADTSE）脉冲序列可以在多个回声时间（TES）和定量T2 MAP提供高分辨率的复合图像，T2加权图像，全部来自单个K空间审查。在这项工作中，我们使用深度学习的卷积神经网络（CNN）进行腹部RADTSE图像中的肝脏分割。实施了具有广义骰子丢失目标功能的修改后的UNET体系结构。训练了三个2D CNN，每种图像类型从RADTSE序列获得。在评估CNN在验证集中的性能时，我们发现在TE图像或T2映射的CNN中，CNN的平均骰子得分高于复合图像。反过来，这意味着有关组织中T2变化的信息有助于改善分割性能。
Motion-robust 2D Radial Turbo Spin Echo (RADTSE) pulse sequence can provide a high-resolution composite image, T2-weighted images at multiple echo times (TEs), and a quantitative T2 map, all from a single k-space acquisition. In this work, we use a deep-learning convolutional neural network (CNN) for the segmentation of liver in abdominal RADTSE images. A modified UNET architecture with generalized dice loss objective function was implemented. Three 2D CNNs were trained, one for each image type obtained from the RADTSE sequence. On evaluating the performance of the CNNs on the validation set, we found that CNNs trained on TE images or the T2 maps had higher average dice scores than the composite images. This, in turn, implies that the information regarding T2 variation in tissues aids in improving the segmentation performance.
