Abstract：
The vessel-like structure in biomedical images, such as within cerebrovascular and nervous pathologies, is an essential biomarker in understanding diseases\' mechanisms and in diagnosing and treating diseases. However, existing vessel-like structure segmentation methods often produce unsatisfactory results due to challenging segmentations for crisp edges. The edge and nonedge voxels of the vessel-like structure in three-dimensional (3D) medical images usually have a highly imbalanced distribution as most voxels are non-edge, making it challenging to find crisp edges. In this work, we propose a generic neural network for the segmentation of the vessel-like structures in different 3D medical imaging modalities. The new edge-reinforced neural network (ER-Net) is based on an encoder-decoder architecture. Moreover, a reverse edge attention module and an edge-reinforced optimization loss are proposed to increase the weight of the voxels on the edge of the given 3D volume to discover and better preserve the spatial edge information. A feature selection module is further introduced to select discriminative features adaptively from an encoder and decoder simultaneously, which aims to increase the weight of edge voxels, thus significantly improving the segmentation performance. The proposed method is thoroughly validated using four publicly accessible datasets, and the experimental results demonstrate that the proposed method generally outperforms other state-of-the-art algorithms for various metrics.
摘要：
生物医学图像中的血管状结构，例如在脑血管和神经病理学中，是理解疾病机制以及诊断和治疗疾病的重要生物标志物。然而,现有的血管状结构分割方法往往产生不满意的结果，由于具有挑战性的分割清晰的边缘。三维（3D）医学图像中的血管状结构的边缘和非边缘体素通常具有高度不平衡的分布，因为大多数体素是非边缘的，使其具有挑战性，以找到脆的边缘。在这项工作中,我们提出了一个通用的神经网络，用于在不同的3D医学成像模式中分割血管状结构。新的边缘增强神经网络（ER-Net）基于编码器-解码器架构。此外，反向边缘注意模块和边缘增强优化损失被提出来增加给定3D体积的边缘上的体素的权重,以发现和更好地保存空间边缘信息。还引入了特征选择模块，用于同时从编码器和解码器中自适应地选择有区别的特征。旨在增加边缘体素的重量，从而显著提高分割性能。使用四个可公开访问的数据集彻底验证了所提出的方法，实验结果表明，所提出的方法在各种度量方面通常优于其他最新的算法。
