Electrical Engineering and Systems Science > Image and Video Processing
[Submitted on 7 Jul 2022 (v1), last revised 14 Jul 2022 (this version, v3)]
Title:What Makes for Automatic Reconstruction of Pulmonary Segments
View PDFAbstract:3D reconstruction of pulmonary segments plays an important role in surgical treatment planning of lung cancer, which facilitates preservation of pulmonary function and helps ensure low recurrence rates. However, automatic reconstruction of pulmonary segments remains unexplored in the era of deep learning. In this paper, we investigate what makes for automatic reconstruction of pulmonary segments. First and foremost, we formulate, clinically and geometrically, the anatomical definitions of pulmonary segments, and propose evaluation metrics adhering to these definitions. Second, we propose ImPulSe (Implicit Pulmonary Segment), a deep implicit surface model designed for pulmonary segment reconstruction. The automatic reconstruction of pulmonary segments by ImPulSe is accurate in metrics and visually appealing. Compared with canonical segmentation methods, ImPulSe outputs continuous predictions of arbitrary resolutions with higher training efficiency and fewer parameters. Lastly, we experiment with different network inputs to analyze what matters in the task of pulmonary segment reconstruction. Our code is available at this https URL.
Submission history
From: Kaiming Kuang [view email][v1] Thu, 7 Jul 2022 04:24:17 UTC (805 KB)
[v2] Tue, 12 Jul 2022 03:15:35 UTC (806 KB)
[v3] Thu, 14 Jul 2022 13:51:02 UTC (806 KB)
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