Modeling a collapsed lung using CT data

A system for modeling lungs of a patient, comprising:

• a surgical instrument;
• a position sensor coupled to the surgical instrument;
• a processor in communication with the position sensor;
• and
• a memory in communication with the processor and having stored thereon instructions, which, when executed by the processor, cause the processor to:
• receive computed tomography (CT) data of a lung of a patient;
• segment tissue shown in the CT data, yielding segmented tissue;
• generate a three-dimensional (3D) model of the lung of the patient based on the segmented tissue;
• apply properties to the segmented tissue of the 3D model;
• calculate a displacement of the lung in a collapsed state based on the 3D model;
• generate a collapsed lung model based on the displacement of the lung of the patient;
• receive position data from a surgical instrument being advanced within a thoracic cavity of the patient,
• identify feature points within an actual collapsed lung of the patient based on the position data;
• calculate an offset between feature points within the actual collapsed lung of the patient and corresponding feature points within the 3D model of the lung of the patient;
• recalculate the displacement of the lung of the patient based on the offset, yielding a recalculated displacement;
• and
• regenerate the collapsed lung model based on the recalculated displacement.

A method comprising:

• receiving computed tomography (CT) data of a lung of a patient;
• segmenting tissue shown in the CT data, yielding segmented tissue;
• generating a three-dimensional (3D) model of the lung of the patient based on the segmented tissue;
• applying properties to the segmented tissue of the 3D model;
• calculating a displacement of the lung in a collapsed state based on the 3D model;
• generating a collapsed lung model based on the displacement of the lung of the patient;
• receiving position data from a surgical instrument being advanced within a thoracic cavity of the patient,
• identifying feature points within an actual collapsed lung of the patient based on the position data;
• calculating an offset between feature points within the actual collapsed lung of the patient and corresponding feature points within the 3D model of the lung of the patient;
• recalculating the displacement of the lung of the patient based on the offset, yielding a recalculated displacement;
• and
• regenerating the collapsed lung model based on the recalculated displacement.