用神经隐式函数变形模板，实现高精度肺段重建。
Template-Guided Reconstruction of Pulmonary Segments with Neural Implicit Functions
- 以可学习模板为基底，通过隐式函数建模肺段表面。
- 在800个标注肺段数据上达到优于现有方法的精度。
- 适合肺癌手术规划与三维重建研究者使用。
高质量的肺段三维重建对肺段切除术和肺癌手术规划至关重要。由于目标重建需高分辨率，传统深度学习方法常受限于计算资源或粒度不足。相比之下，隐式建模因其计算高效且支持任意分辨率连续表示而更受青睐。本文提出一种基于神经隐式函数的方法，通过形变可学习模板来学习3D表面，实现解剖感知的精确肺段重建。同时引入两个临床相关的评估指标，全面衡量重建质量。此外，针对公开可用形状数据集的缺乏，构建了名为Lung3D的数据集，包含800个标注肺段及其对应气道、动脉、静脉和段间静脉的3D模型。实验表明，所提方法优于现有技术，为肺段重建提供了新视角。代码与数据将公开于https://github.com/HINTLab/ImPulSe。
原文摘要 · Abstract (English)
High-quality 3D reconstruction of pulmonary segments plays a crucial role in segmentectomy and surgical planning for the treatment of lung cancer. Due to the resolution requirement of the target reconstruction, conventional deep learning-based methods often suffer from computational resource constraints or limited granularity. Conversely, implicit modeling is favored due to its computational efficiency and continuous representation at any resolution. We propose a neural implicit function-based method to learn a 3D surface to achieve anatomy-aware, precise pulmonary segment reconstruction, represented as a shape by deforming a learnable template. Additionally, we introduce two clinically relevant evaluation metrics to comprehensively assess the quality of the reconstruction. Furthermore, to address the lack of publicly available shape datasets for benchmarking reconstruction algorithms, we developed a shape dataset named Lung3D, which includes the 3D models of 800 labeled pulmonary segments and their corresponding airways, arteries, veins, and intersegmental veins. We demonstrate that the proposed approach outperforms existing methods, providing a new perspective for pulmonary segment reconstruction. Code and data will be available at https://github.com/HINTLab/ImPulSe.
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