DPN: Detail-Preserving Network with High Resolution Representation for Efficient Segmentation of Retinal Vessels

Retinal vessels are important biomarkers for many ophthalmological and cardiovascular diseases. It is of great significance to develop an accurate and fast vessel segmentation model for computer-aided diagnosis.Existing methods, such as U-Net follows the encoder-decoder pipeline, where detailed information is lost in the encoder in order to achieve a large field of view. Although detailed information could be recovered in the decoder via multi-scale fusion, it still contains noise. In this paper, we propose a deep segmentation model, called detail-preserving network (DPN) for efficient vessel segmentation. To preserve detailed spatial information and learn structural information at the same time, 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. As a result, the DPN could maintain a high resolution during the processing, which is helpful to locate the boundaries of thin vessels. To illustrate the effectiveness of our method, we conducted experiments over three public datasets. Experimental results show, compared to state-of-the-art methods, our method shows competitive/better performance in terms of segmentation accuracy, segmentation speed, extensibility and the number of parameters. Specifically, 1) the AUC of our method ranks first/second/third on the STARE/CHASE_DB1/DRIVE datasets, respectively. 2) Only one forward pass is required of our method to generate a vessel segmentation map, and the segmentation speed of our method is over 20-160x faster than other methods on the DRIVE dataset. 3) We conducted cross-training experiments to demonstrate the extensibility of our method, and results revealed that our method shows superior performance. 4) The number of parameters of our method is only around 96k, less then all comparison methods.

DPN：具有高分辨率表示的细节保存网络，可对视网膜血管进行有效分割

视网膜血管是许多眼科和心血管疾病的重要生物标志物。开发准确，快速的血管分割模型用于计算机辅助诊断具有重要意义。.. 现有的方法（例如U-Net）遵循编码器-解码器管线，在该管线中，详细信息会丢失在编码器中，以实现较大的视野。尽管可以通过多尺度融合在解码器中恢复详细信息，但它仍然包含噪声。在本文中，我们提出了一种深度分割模型，称为细节保留网络（DPN），用于有效的血管分割。为了同时保留详细的空间信息和学习结构信息，我们设计了详细信息保留块（DP-Block）。此外，我们将八个DP块堆叠在一起以形成DPN。更重要的是，这些模块之间不存在下采样操作。结果，DPN可以在处理过程中保持高分辨率，这有助于定位细血管的边界。为了说明我们方法的有效性，我们对三个公共数据集进行了实验。实验结果表明，与最新方法相比，我们的方法在分割精度，分割速度，可扩展性和参数数量方面显示出竞争/更好的性能。具体来说，1）我们的方法的AUC在STARE / CHASE_DB1 / DRIVE数据集上分别排名第一/第二/第三。2）我们的方法只需要一个向前通过就可以生成血管分割图，并且该方法的分割速度比DRIVE数据集上的其他方法快20-160倍。3）我们进行了交叉训练实验以证明我们方法的可扩展性，结果表明我们的方法表现出了优越的性能。4）我们方法的参数数量只有96k左右， （阅读更多）