本文的第一作者王搏，题目为“ Boundary Aware U-Net for Retinal Layers Segmentation in Optical Coherence Tomography Images”，发表于IEEE Journal of Biomedical and Health Informatics 

以下为文章摘要：

英文摘要：

Retinal layers segmentation in optical coherence tomography (OCT) images is a critical step in the diagnosis of numerous ocular diseases. Automatic layers segmentation requires separating each individual layer instance with accurate boundary detection, but remains a challenging task since it suffers from speckle noise, intensity inhomogeneity, and the low contrast around boundary. In this work, we proposed a boundary aware U-Net (BAU-Net) for retinal layers segmentation by detecting accurate boundary. Based on encoder-decoder architecture, we design a dual tasks framework with low-level outputs for boundary detection and high-level outputs for layers segmentation. Specifically, we first use the multi-scale input strategy to enrich the spatial information in the deep features of encoder. For low-level features from encoder, we design an edge aware (EA) module in skip connection to extract the pure edge features. Then, a U-structure feature enhanced (UFE) module is designed in all skip connections to enlarge the features receptive fields from the encoder. Besides, a canny edge fusion (CEF) module is introduced to aforementioned architecture, which can fuse the priory edge information from segmentation task to boundary detection branch for a better predication. Furthermore, we model each boundary as a vertical coordinates distribution for boundary detection. Based on this distribution, a topology guarantee loss with combined A-scan regression loss and structure loss is proposed to make an accurate and guaranteed topological boundary set. The method is evaluated on two public datasets and the results demonstrate that the BAU-Net achieves promising performance than other state-of-the-art methods.

中文：

光学相干断层扫描（OCT）图像中的视网膜层分割是许多眼科疾病诊断的关键步骤。自动层分割需要用精确的边界检测来分离每个实例层，但是由于存在斑点噪声、强度不均匀性和边界对比度低等问题，仍然是一个具有挑战性的任务。在这项工作中，我们提出了一个边界感知U-Net网络，然后通过检测准确的边界来进行视网膜组织层分割。基于编码器-解码器结构，我们设计了一个双任务框架，低层输出用于边界检测，高层输出用于组织层分割。具体来说，我们首先采用多尺度输入策略来丰富编码器深层特征中的空间信息。针对编码器的低层特征，我们在跳层连接中设计了一个边缘感知模块来提取纯的边缘特征。然后，在所有的跳层连接中设计了一个U型特征增强模块，以扩大编码器的感受野。此外，在上述体系结构中还引入了canny边缘融合模块，即将分割任务中的先验边缘信息融合到边界检测分支中，以达到更好的预测效果。此外，我们将每个边界建模为一个垂直坐标分布来进行边界检测。基于此分布，提出了一种将A扫描回归损失和结构损失相结合的拓扑保证损失，以得到一个精确且保证拓扑的边界集。在两个公共数据集上对该方法进行了评价，结果表明，边界感知U-Net比其他最先进的方法具有更好的性能。

文章链接：https://ieeexplore.ieee.org/document/9380449