In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance.

本文提出了一种基于U-Net网络的扩展，Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net)，用于医学图像分割，充分利用了U-Net、双向ConvLSTM和密集卷积的机制。通过在跳跃连接中使用BConvLSTM以非线性的方式组合对应的编码路径和先前解码上升卷积层提取的特征图，并在编码路径的最后一个卷积层中使用密集连接卷积来加强特征传播和特征复用，最终通过批量归一化 (BN)来加速所提出的模型的收敛速度。在视网膜血管分割、皮肤病变分割和肺结节分割三个数据集上进行评估，实现了与最先进性能的匹配。

具有密集连接卷积的双向ConvLSTM U-Net