School of Software, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Department of Cardiovascular Medicine, Second Affiliated Hospital of Xi'an, Jiaotong University, Xi'an, Shaanxi, China.
Technol Health Care. 2023;31(S1):347-355. doi: 10.3233/THC-236030.
Coronary artery disease (CAD) manifests with a blockage the coronary arteries, usually due to plaque buildup, and has a serious impact on the human life. Atherosclerotic plaques, including fibrous plaques, lipid plaques, and calcified plaques can lead to occurrence of CAD. Optical coherence tomography (OCT) is employed in the clinical practice as it clearly provides a detailed display of the lesion plaques, thereby assessing the patient's condition. Analyzing the OCT images manually is a very tedious and time-consuming task for the clinicians. Therefore, automatic segmentation of the coronary OCT images is necessary.
In view of the good utility of Unet network in the segmentation of medical images, the present study proposed the development of a Unet network based on Sk-Conv and spatial pyramid pooling modules to segment the coronary OCT images.
In order to extract multi-scale features, these two modules were added at the bottom of UNet. Meanwhile, ablation experiments are designed to verify each module is effective.
After testing, our model achieves 0.8935 on f1 score and 0.7497 on mIOU. Compared to the current advanced models, our model performs better.
Our model achieves good results on OCT sequences.
冠状动脉疾病 (CAD) 表现为冠状动脉阻塞，通常是由于斑块积聚所致，对人类生命有严重影响。动脉粥样硬化斑块包括纤维斑块、脂质斑块和钙化斑块，可导致 CAD 的发生。光学相干断层扫描 (OCT) 在临床实践中得到应用，因为它能清晰地提供病变斑块的详细显示，从而评估患者的病情。手动分析 OCT 图像对于临床医生来说是一项非常繁琐和耗时的任务。因此，需要对冠状动脉 OCT 图像进行自动分割。
鉴于 U-Net 网络在医学图像分割中的良好应用，本研究提出了一种基于 Sk-Conv 和空间金字塔池化模块的 U-Net 网络，用于分割冠状动脉 OCT 图像。
为了提取多尺度特征，在 U-Net 的底部添加了这两个模块。同时，设计了消融实验来验证每个模块的有效性。
经过测试，我们的模型在 f1 分数上达到 0.8935，在 mIOU 上达到 0.7497。与当前先进的模型相比，我们的模型表现更好。
我们的模型在 OCT 序列上取得了良好的效果。
