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A multi view multi stage and multi window framework for pulmonary artery segmentation from CT scans

arXiv 2209.03918 ↗First posted 8 Sep 2022eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 6 references that are in the map. First posted on arXiv 8 Sep 2022.

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References · 3

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  3. 2017A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentationcited by 20

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