改进双向LSTM的肺结节分割方法

基于深度学习网络在医学图像分割方面取得了很多成果。由于类圆形的肺结节不同于血管和大部分肺部结构呈扁平状，因此通过对U-Net进行扩展，提出一种带有多视图密集卷积的双向LSTM U-Net网络来消除血管和肺内组织结构以检测结节。与U-Net在跳跃连接中进行级联不同，改进双向LSTM网络将编码路径中提取特征图与解码卷积层进行非线性结合。为了加强特征传播和鼓励特征复用，在编码路径的最后一个卷积层采用密集卷积，最后使用批处理规范化（BN）来加速网络的收敛速度。实验结果表明该模型有效地提高了肺结节分割的准确率，对LUNA16和阿里巴巴天池竞赛数据集中每个候选样本提取轴位、冠状和矢状视图后训练的MIoU达到了90.1%。

Improved Bi-LSTM Segmentation Method of Lung Nodules

Many achievements have made in medical image segmentation based on deep learning networks. Because the round-shaped lung nodules are different from blood vessels and most of the lung structures are flat, a Bi-LSTM U-Net network with multi-view dense convolution is proposed to eliminate blood vessels by expanding U-Net and lung tissue structure to detect nodules. Unlike U-Net cascading in skip connections, the improved Bi-LSTM network combines the extracted feature map in the encoding path with the decoded convolutional layer for non-linear combination. In order to enhance feature propagation and encourage feature reuse, dense convolution is used in the last convolutional layer of the encoding path, and finally Batch Normalization（BN） is used to accelerate the convergence speed of the network. Experimental results show that the model effectively improves the accuracy of lung nodule segmentation, and the MIoU trained after extracting the axial position, coronal and sagittal views of each candidate sample in the LUNA16 and Alibaba Tianchi competition data sets reaches 90.1%.