论文标题
DPN：具有高分辨率表示的细节保护网络，可有效分割视网膜血管
DPN: Detail-Preserving Network with High Resolution Representation for Efficient Segmentation of Retinal Vessels
论文作者
论文摘要
视网膜血管是许多眼科和心血管疾病的重要生物标志物。因此，开发用于计算机辅助诊断的自动模型具有重要意义。现有方法（例如U-NET）遵循编码器少数管道，其中详细信息在编码器中丢失，以实现大型视野。尽管空间详细信息可以在解码器中部分恢复，而编码器的高分辨率特征图中有噪声。而且，我们认为这种编码器架构对血管分割的效率低下。在本文中，我们介绍了详细信息的网络（DPN），该网络避免了编码器decoder管道。为了保留详细信息并同时学习结构信息，我们设计了详细信息块（DP-block）。此外，我们将八个DP块堆在一起以形成DPN。更重要的是，这些块之间没有下采样操作。因此，DPN可以在处理过程中保持高/完整的分辨率，从而避免详细信息的丢失。为了说明DPN的有效性，我们对三个公共数据集进行了实验。实验结果表明，与最先进的方法相比，DPN在分割精度，分割速度和模型大小方面显示出竞争性/更好的性能。具体来说，1）我们的方法在驱动器，chase_db1和HRF数据集上实现了可比的分割性能。 2）DPN的分割速度比驱动器数据集上的其他方法快20-160倍。 3）DPN IS1的参数数量左右，远远少于所有比较方法。
Retinal vessels are important biomarkers for many ophthalmological and cardiovascular diseases. Hence, it is of great significance to develop automatic models for computer-aided diagnosis. Existing methods, such as U-Net follow the encoder-decoder pipeline, where detailed information is lost in the encoder in order to achieve a large field of view. Although spatial detailed information could be recovered partly in the decoder, while there is noise in the high-resolution feature maps of the encoder. And, we argue this encoder-decoder architecture is inefficient for vessel segmentation. In this paper, we present the detail-preserving network (DPN), which avoids the encoder-decoder pipeline. To preserve detailed information and learn structural information simultaneously, we designed the detail-preserving block (DP-Block). Further, we stacked eight DP-Blocks together to form the DPN. More importantly, there are no down-sampling operations among these blocks. Therefore, the DPN could maintain a high/full resolution during processing, avoiding the loss of detailed information. To illustrate the effectiveness of DPN, we conducted experiments over three public datasets. Experimental results show, compared to state-of-the-art methods, DPN shows competitive/better performance in terms of segmentation accuracy, segmentation speed, and model size. Specifically, 1) Our method achieves comparable segmentation performance on the DRIVE, CHASE_DB1, and HRF datasets. 2) The segmentation speed of DPN is over 20-160 times faster than other methods on the DRIVE dataset. 3) The number of parameters of DPN is1 around 120k, far less than all comparison methods.
