Template-Guided Reconstruction of Pulmonary Segments with Neural Implicit Functions
This work addresses the need for precise pulmonary segment reconstruction in surgical planning for lung cancer patients, representing an incremental improvement with a novel method for a known bottleneck.
The authors tackled the problem of high-quality 3D reconstruction of pulmonary segments for lung cancer surgery by proposing a neural implicit function-based method that deforms a learnable template, and they demonstrated that it outperforms existing methods.
High-quality 3D reconstruction of pulmonary segments plays a crucial role in segmentectomy and surgical treatment planning for 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 assess the reconstruction comprehensively. Further, due to the absence of publicly available shape datasets to benchmark reconstruction algorithms, we developed a shape dataset named Lung3D, including the 3D models of 800 labeled pulmonary segments and the 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/M3DV/ImPulSe.