Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty for the complex segmentation task due to different issues like various image appearance, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper background illumination. To overcome these challenges, we present a unique curvilinear structure segmentation framework based on oriented derivative of stick (ODoS) filter and deep learning network for curvilinear object segmentation in medical images. Currently, a large number of deep learning models emphasis on developing deep architectures and ignore capturing the structural features of curvature objects, which may lead to unsatisfactory results. In consequence, a new approach that incorporates the ODoS filter as part of a deep learning network is presented to improve the spatial attention of curvilinear objects. In which, the original image is considered as principal part to describe various image appearance and complex background illumination, the multi-step strategy is used to enhance contrast between curvilinear objects and their surrounding backgrounds, and the vector field is applied to discriminate thin and uneven curvilinear structures. Subsequently, a deep learning framework is employed to extract varvious structural features for curvilinear object segmentation in medical images. The performance of the computational model was validated in experiments with publicly available DRIVE, STARE and CHASEDB1 datasets. Experimental results indicate that the presented model has yielded surprising results compared with some state-of-the-art methods.

翻译：为了克服这些挑战,我们提出了一个独特的曲线结构分割框架,其基础是树枝过滤器和深层学习网络的定向衍生物,用于诊断和评估人类疾病,然而,由于不同的问题,例如各种图像外观、曲线天体及其周围背景之间的低差异、卷尾线结构薄和不均衡以及不适当的背景照明等不同问题,对于复杂的分解任务而言,这是一个具有挑战性的不确定性。为了克服这些挑战,我们提出了一个独特的曲线结构分割框架,其基础是树枝过滤器和深层学习网络的定向衍生物(OdoS)过滤器和医学图象中曲线天体分解。目前,大量深层学习模型强调开发深层结构,忽视采集曲线天体的结构特征,这可能会导致不令人满意的结果。因此,介绍了一种将ODoS过滤器作为深层学习网络的一部分的新方法,以提高卷轴天体物体的空间关注度。 原始图像被视为描述各种图像外观和复杂的模型底底底底线显示的主要部分,多步骤战略用于加强曲线天体对象及其周围背景之间的对比,而矢量场则用于将曲线的递归物体的分流体分析,在深层计算过程中采用一种可分析的递取的递性计算结果结构结构结构结构图结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构。