Why the study?
Pulmonary embolism and other pulmonary vascular diseases are associated with arterial morphological changes, motivating fully automatic methods to extract the pulmonary arterial tree from CTPA images.
Does an automatic segmentation method based on fuzzy connectedness improve the accuracy of pulmonary arterial tree extraction from CTPA scans compared to machine learning?
Does an automatic segmentation method based on fuzzy connectedness improve the accuracy of pulmonary arterial tree extraction from CTPA scans compared to machine learning?
A novel automatic segmentation method for pulmonary arterial trees in CTPA scans achieves high accuracy, potentially aiding in the computer-aided diagnosis of pulmonary embolism.
May aid automated PE diagnosis via CTPA segmentation; leaves open validation in larger cohorts.
Pulmonary embolism (PE) and other pulmonary vascular diseases, have been found associated with the changes in arterial morphology. To detect arterial changes, we propose a novel, fully automatic method that can extract pulmonary arterial tree in computed tomographic pulmonary angiography (CTPA) images. The approach is based on the fuzzy connectedness framework, combined with 3D vessel enhancement and Harris Corner detection to achieve accurate segmentation. The effectiveness and robustness of the method is validated in clinical datasets consisting of 10 CT angiography scans (6 without PE and 4 with PE). The performance of our method is compared with manual classification and machine learning method based on random forest. Our method achieves a mean accuracy of 92% when compared to manual reference, which is higher than the 89% accuracy achieved by machine learning. This performance of the segmentation for pulmonary arteries may provide a basis for the CAD application of PE.
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Zhang et al. (2019) studied this question.
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