Automatic Extraction and Anatomical Labeling of the Airway Tree from Inspiration and Expiration Thoracic CT Scans

PURPOSE To develop and validate a robust technique for automatic extraction and labeling of the airway tree from thoracic CT scans. METHOD AND MATERIALS Three sets of 50 scans were used. For set 1, 50 heavy smokers underwent low-dose CT (16x0.75mm, 30 mAs, 120-140 kVp, full inspiration) for a lung cancer screening trial. For set 2, the same 50 subjects were scanned again in full expiration at ultra low-dose (20 mAs, 90 kVp). Set 3 comprised 50 scans, many including gross pulmonary opacifications, from patients with interstitial lung disease. These were acquired at clinical dose (120-170 mAs, 120 kVp), in full inspiration, with and without contrast material. The method starts in the trachea to grow the airways, using locally adaptive criteria for accepting voxels as part of a bronchus. While growing, the branch centerline, orientation and diameter are tracked and bifurcations are detected. A rule set based on diameter and orientation of the current segment and its parent determines if a potential segment is accepted, or discarded as leakage. When extraction is completed, the 32 central bronchi are labeled automatically based on parent-child relationships. Incorrect segments were visually identified. The number, generation and total length of extracted airways was evaluated. RESULTS The method required around 20 seconds of computation time and segmentations contained almost no false positive segments (<1%). In Set 1 and 3 on average 166 and 174 branches were extracted with an average total length of 2183 and 1949 mm. For the noisy expiration data in Set 2 this was substantially less: 59 segments and 789 mm. After the 6th generation, on average less segments were extracted but usually the tree included some segments up to the 10th generation. Central bronchi were found in 93% of all cases. CONCLUSION Fast and automatic extraction of airway tree including most central bronchi and many peripheral bronchi is feasible in inspiration CT scans, but challenging in expiration data. CLINICAL RELEVANCE/APPLICATION Quantitative descriptors of airway morphology are essential to measure progression of lung diseases such as COPD, asthma, CF, ILD. This requires robust automatic extraction of the airway tree.