What Makes for Automatic Reconstruction of Pulmonary Segments
- URL: http://arxiv.org/abs/2207.03078v1
- Date: Thu, 7 Jul 2022 04:24:17 GMT
- Title: What Makes for Automatic Reconstruction of Pulmonary Segments
- Authors: Kaiming Kuang, Li Zhang, Jingyu Li, Hongwei Li, Jiajun Chen, Bo Du,
Jiancheng Yang
- Abstract summary: 3D reconstruction of pulmonary segments plays an important role in surgical treatment planning of lung cancer.
However, automatic reconstruction of pulmonary segments remains unexplored in the era of deep learning.
We propose ImPulSe, a deep implicit surface model designed for pulmonary segment reconstruction.
- Score: 50.216231776343115
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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
https://github.com/M3DV/ImPulSe.
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