High Accuracy Pulmonary Vessel Segmentation for Contrast and Non-contrast CT Images and Its Clinical Evaluation

• URL: http://arxiv.org/abs/2503.16988v1
• Date: Fri, 21 Mar 2025 09:54:42 GMT
• Title: High Accuracy Pulmonary Vessel Segmentation for Contrast and Non-contrast CT Images and Its Clinical Evaluation
• Authors: Ying Ming, Shaoze Luo, Longfei Zhao, Qiqi Xu, Wei Song,
• Abstract summary: We propose a 3D image segmentation algorithm for automated pulmonary vessel segmentation from both contrast and non-contrast CT images.<n>In this work, we used 427 sets of high-precision annotated CT data from multiple vendors and countries.<n>Our model has achieved good performance in both accuracy and completeness of pulmonary vessel segmentation.
• Score: 2.888317341648462
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate segmentation of pulmonary vessels plays a very critical role in diagnosing and assessing various lung diseases. In clinical practice, diagnosis is typically carried out using CTPA images. However, there is a lack of high-precision pulmonary vessel segmentation algorithms for CTPA, and pulmonary vessel segmentation for NCCT poses an even greater challenge. In this study, we propose a 3D image segmentation algorithm for automated pulmonary vessel segmentation from both contrast and non-contrast CT images. In the network, we designed a Vessel Lumen Structure Optimization Module (VLSOM), which extracts the centerline of vessels and adjusts the weights based on the positional information and adds a Cl-Dice-Loss to supervise the stability of the vessels structure. In addition, we designed a method for generating vessel GT from CTPA to NCCT for training models that support both CTPA and NCCT. In this work, we used 427 sets of high-precision annotated CT data from multiple vendors and countries. Finally, our experimental model achieved Cl-Recall, Cl-DICE and Recall values of 0.879, 0.909, 0.934 (CTPA) and 0.928, 0.936, 0.955 (NCCT) respectively. This shows that our model has achieved good performance in both accuracy and completeness of pulmonary vessel segmentation. In clinical visual evaluation, our model also had good segmentation performance on various disease types and can assist doctors in medical diagnosis, verifying the great potential of this method in clinical application.

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