This research develops a 3D modeling approach to comprehensively visualize multiple pulmonary nodules throughout the whole lung, aiming to improve the diagnosis and the treatment of early-stage lung cancer patients. The key questions are how to accurately reconstruct nodule distribution and interplay with lung tissue. Recent advance in deep learning and computer vision enable more accurate AI-assisted detection and segmentation of lung nodules.
However, limitation persist regarding whole lung modeling and the spatial relationships between multiple nodules. This research offers progress through a 3D reconstruction technique for the entire lung. Integrating AI-driven medical imaging and visualization with specialist clinical diagnosis and treatment is critical.
Technologies like deep learning for segmentation volumetric modeling, virtual augmented reality for 3D visualization and multimodal data fusion are advancing whole lung modeling and multinodule assessment to enhance clinical decision-making. This research establish a effective 3D modeling approach for visualizing the distribution and the spatial relationships of multiple pulmonary nodules across the whole lung volume. Key innovations include lung contour extraction, nodule reconstruction in 3D space, and interactive whole lung visualization.
This enables more accurate diagnosis and treatment planning for early-lung cancer patients. Developing a accurate 3D modeling approach for visualizing whole lung nodule patterns provide a new capability for comprehending disease progression. This can enable earlier diagnosis, personalize the treatment plans and improve the outcomes for lung cancer patients.
The findings lay the groundwork for expanding whole lung modeling using multimodal data and advancing clinical translation.
