A multi view multi stage and multi window framework for pulmonary artery segmentation from CT scans
This work addresses segmentation accuracy for medical imaging applications, but it is incremental as it builds on existing methods without major breakthroughs.
The authors tackled pulmonary artery segmentation from CT scans by developing a two-stage 3D CNN framework with multi-view and multi-window methods, achieving 9th place in the PARSE2022 Challenge.
This is the technical report of the 9th place in the final result of PARSE2022 Challenge. We solve the segmentation problem of the pulmonary artery by using a two-stage method based on a 3D CNN network. The coarse model is used to locate the ROI, and the fine model is used to refine the segmentation result. In addition, in order to improve the segmentation performance, we adopt multi-view and multi-window level method, at the same time we employ a fine-tune strategy to mitigate the impact of inconsistent labeling.