Deep Learning for Lung Localization in Real-time

Stanford researchers have developed deep learning methods which can more precisely localize the position and orientation of a camera in the lung anatomy in real-time. Physicians will be able to more accurately navigate the lung's branching airways for either diagnostic or therapeutic purposes. This method uses deep learning with convolutional neural networks (CNN) to localize the bronchoscope with respect to the lung CT scan using camera images and device insertion information. Position sensor data is not required. Additionally, the CNN is trained entirely on simulated images derived from the patient-specific CT. Precise localization is critical for bronchoscopies, robotic bronchoscopies, and other lung procedures.

Video

Stage of Development
Proof-of-concept - Accuracy demonstrated on a full-size human lung model