Abstract：
Changes in the structure and function of nailfold capillaries may be indicators of numerous diseases. Noninvasive diagnostic tools are commonly used for the extraction of morphological information from segmented nailfold capillaries to study physiological and pathological changes therein. However, current segmentation methods for nailfold capillaries cannot accurately separate capillaries from the background, resulting in issues such as unclear segmentation boundaries. Therefore, improving the accuracy of nailfold capillary segmentation is necessary to facilitate more efficient clinical diagnosis and research. Herein, we propose a nailfold capillary image segmentation method based on a U2-Net backbone network combined with a Transformer structure. This method integrates the U2-Net and Transformer networks to establish a decoder-encoder network, which inserts Transformer layers into the nested two-layer U-shaped architecture of the U2-Net. This structure effectively extracts multiscale features within stages and aggregates multilevel features across stages to generate high-resolution feature maps. The experimental results demonstrate an overall accuracy of 98.23 %, a Dice coefficient of 88.56 %, and an IoU of 80.41 % compared to the ground truth. Furthermore, our proposed method improves the overall accuracy by approximately 2 %, 3 %, and 5 % compared to the original U2-Net, Res-Unet, and U-Net, respectively. These results indicate that the Transformer-U2Net network performs well in nailfold capillary image segmentation and provides more detailed and accurate information on the segmented nailfold capillary structure, which may aid clinicians in the more precise diagnosis and treatment of nailfold capillary-related diseases.
摘要：
甲皱毛细血管的结构和功能的变化可能是许多疾病的指标。非侵入性诊断工具通常用于从分段的甲褶毛细血管中提取形态信息以研究其中的生理和病理变化。然而,当前的甲皱毛细血管分割方法不能准确地将毛细血管从背景中分离出来，导致分割边界不清等问题。因此,为了促进更有效的临床诊断和研究，有必要提高甲皱毛细血管分割的准确性。在这里，提出了一种基于U2-Net骨干网结合变压器结构的甲折毛细管图像分割方法。该方法集成U2-Net和Transformer网络，建立解码器-编码器网络，它将Transformer层插入U2-Net的嵌套两层U形体系结构中。这种结构有效地在阶段内提取多尺度特征，并跨阶段聚合多级特征以生成高分辨率特征图。实验结果表明，总体准确率为98.23%，a骰子系数为88.56%，与实际情况相比，IoU为80.41%。此外，我们提出的方法将整体精度提高了约2%，3%,与原始U2-Net相比，为5%，Res-Unet,和U-Net，分别。这些结果表明，Transformer-U2Net网络在甲折毛细管图像分割中表现良好，并提供了有关分割的甲折毛细管结构的更详细和准确的信息，这可以帮助临床医生更精确地诊断和治疗甲皱毛细血管相关疾病。
