Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110189, China.
College of Computer Science and Engineering, Northeastern University, Shenyang 110189, China.
J Healthc Eng. 2021 Nov 5;2021:5763177. doi: 10.1155/2021/5763177. eCollection 2021.
Segmentation of pulmonary vessels in CT/CTA images can help physicians better determine the patient's condition and treatment. However, due to the complexity of CT images, existing methods have limitations in the segmentation of pulmonary vessels. In this paper, a method based on the separation of pulmonary vessels in CT/CTA images is investigated. The method is divided into two steps: in the first step, the lung parenchyma is extracted using the Unet++ algorithm, which can effectively reduce the oversegmentation rate; in the second step, the pulmonary vessels in the lung parenchyma are extracted using nnUnet. According to the obtained lung parenchyma segmentation results, the "AND" operation is performed on the original image and the lung parenchyma segmentation results, and only the blood vessels within the lung parenchyma are segmented, which reduces the interference of external tissues and improves the segmentation accuracy. The experimental data source used CT/CTA images acquired from the partner hospital. After the experiments were performed on a total of 67 sets of images, the accuracy of CT and CTA images reached 85.1% and 87.7%, respectively. The comparison of whether to segment the lung parenchyma and with other conventional methods was also performed, and the experimental results showed that the algorithm in this paper has high accuracy.
CT/CTA 图像中的肺血管分割有助于医生更好地判断患者的病情和治疗方案。然而，由于 CT 图像的复杂性，现有方法在肺血管分割方面存在局限性。本文研究了一种基于 CT/CTA 图像中肺血管分离的方法。该方法分为两步：第一步，使用 Unet++算法提取肺实质，可以有效降低过分割率；第二步，使用 nnUnet 提取肺实质中的肺血管。根据获得的肺实质分割结果，对原始图像和肺实质分割结果进行“AND”操作，仅分割肺实质内的血管，减少了外部组织的干扰，提高了分割精度。实验数据来源为合作医院采集的 CT/CTA 图像。对总共 67 组图像进行实验后，CT 和 CTA 图像的准确率分别达到 85.1%和 87.7%。还比较了是否分割肺实质以及与其他常规方法的效果，实验结果表明，本文算法具有较高的准确性。
