Abstract：
Medical image segmentation commonly involves diverse tissue types and structures, including tasks such as blood vessel segmentation and nerve fiber bundle segmentation. Enhancing the continuity of segmentation outcomes represents a pivotal challenge in medical image segmentation, driven by the demands of clinical applications, focusing on disease localization and quantification. In this study, a novel segmentation model is specifically designed for retinal vessel segmentation, leveraging vessel orientation information, boundary constraints, and continuity constraints to improve segmentation accuracy. To achieve this, we cascade U-Net with a long-short-term memory network (LSTM). U-Net is characterized by a small number of parameters and high segmentation efficiency, while LSTM offers a parameter-sharing capability. Additionally, we introduce an orientation information enhancement module inserted into the model\'s bottom layer to obtain feature maps containing orientation information through an orientation convolution operator. Furthermore, we design a new hybrid loss function that consists of connectivity loss, boundary loss, and cross-entropy loss. Experimental results demonstrate that the model achieves excellent segmentation outcomes across three widely recognized retinal vessel segmentation datasets, CHASE_DB1, DRIVE, and ARIA.
摘要：
医学图像分割通常涉及多种组织类型和结构,包括血管分割和神经纤维束分割等任务。增强分割结果的连续性是医学图像分割的关键挑战，在临床应用需求的推动下，专注于疾病的定位和量化。在这项研究中,一种新颖的分割模型是专门为视网膜血管分割设计的，利用船只方位信息，边界约束,和连续性约束，以提高分割精度。为了实现这一点，我们将U-Net与长短期记忆网络(LSTM)级联。U-Net的特点是参数数量少，分割效率高，而LSTM提供参数共享功能。此外,我们引入了一个方向信息增强模块插入到模型的底层，通过方向卷积算子获得包含方向信息的特征图。此外，我们设计了一个新的混合损失函数，它由连接损失组成，边界损失,和交叉熵损失。实验结果表明，该模型在三个广泛认可的视网膜血管分割数据集上实现了出色的分割结果，CHASE_DB1,DRIVE,还有ARIA.
