Recently, coronary heart disease has attracted more and more attention, where segmentation and analysis for vascular lumen contour are helpful for treatment. And intravascular optical coherence tomography (IVOCT) images are used to display lumen shapes in clinic. Thus, an automatic segmentation method for IVOCT lumen contour is necessary to reduce the doctors’ workload while ensuring diagnostic accuracy. In this paper, we proposed a deep residual segmentation network of multi-scale feature fusion based on attention mechanism (RSM-Network, Residual Squeezed Multi-Scale Network) to segment the lumen contour in IVOCT images. Firstly, three different data augmentation methods including mirror level turnover, rotation and vertical flip are considered to expand the training set. Then in the proposed RSM-Network, U-Net is contained as the main body, considering its characteristic of accepting input images with any sizes. Meanwhile, the combination of residual network and attention mechanism is applied to improve the ability of global feature extraction and solve the vanishing gradient problem. Moreover, the pyramid feature extraction structure is introduced to enhance the learning ability for multi-scale features. Finally, in order to increase the matching degree between the actual output and expected output, the cross entropy loss function is also used. A series of metrics are presented to evaluate the performance of our proposed network and the experimental results demonstrate that the proposed RSM-Network can learn the contour details better, contributing to strong robustness and accuracy for IVOCT lumen contour segmentation.

中文翻译：

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基于注意力机制的多尺度特征融合深度分割网络 IVOCT 流明轮廓

近年来，冠心病越来越受到关注，血管腔轮廓的分割和分析有助于治疗。并且血管内光学相干断层扫描（IVOCT）图像在临床上用于显示管腔形状。因此，需要一种IVOCT管腔轮廓的自动分割方法，以减少医生的工作量，同时保证诊断的准确性。在本文中，我们提出了一种基于注意力机制的多尺度特征融合深度残差分割网络（RSM-Network，Residual Squeezed Multi-Scale Network）来分割IVOCT图像中的管腔轮廓。首先，考虑镜像水平翻转、旋转和垂直翻转三种不同的数据增强方法来扩展训练集。然后在提出的 RSM-Network 中，包含 U-Net 作为主体，考虑到其接受任意尺寸输入图像的特性。同时，将残差网络和注意力机制相结合，提高全局特征提取能力，解决梯度消失问题。此外，引入金字塔特征提取结构以增强多尺度特征的学习能力。最后，为了增加实际输出与期望输出的匹配度，还使用了交叉熵损失函数。提出了一系列指标来评估我们提出的网络的性能，实验结果表明，提出的 RSM 网络可以更好地学习轮廓细节，有助于 IVOCT 流明轮廓分割的强大鲁棒性和准确性。