RATIONALE AND OBJECTIVES: To develop an analytic software package based on automated contour detection for the objective and reproducible assessment of emphysema from computed tomography (CT) scans. METHODS: A semiautomated technique was developed for the definition of lung contours in CT cross-sections followed by the assessment of pulmonary CT parameters describing the disease state. For 78 images, the semiautomated contour detection was performed and compared with contours drawn by an experienced radiologist by calculating the systematic area difference (bias) and differences in pulmonary CT parameters such as the mean lung density (MLD). In addition, intraobserver and interobserver variabilities were determined in a subset of 15 images. RESULTS: The areas enclosed by the semiautomatically detected contours were slightly larger than the manual ones (bias < 2.1%). The biases in the observer studies were smaller in the semiautomated versus the manual case (0.3% vs. 1.3%). The standard deviation of the MLD differences with a manual analysis was larger by a factor of five than in the semiautomated case. On average, manual analysis required 2 minutes, 18 seconds per lung; this time was reduced to 11.5 to 29 seconds with the semiautomated approach, depending on the respiration state. CONCLUSIONS: The semiautomated approach is preferred over the manual approach because of its higher consistency and its shorter analysis time.
RATIONALE AND OBJECTIVES: To develop an analytic software package based on automated contour detection for the objective and reproducible assessment of emphysema from computed tomography (CT) scans. METHODS: A semiautomated technique was developed for the definition of lung contours in CT cross-sections followed by the assessment of pulmonary CT parameters describing the disease state. For 78 images, the semiautomated contour detection was performed and compared with contours drawn by an experienced radiologist by calculating the systematic area difference (bias) and differences in pulmonary CT parameters such as the mean lung density (MLD). In addition, intraobserver and interobserver variabilities were determined in a subset of 15 images. RESULTS: The areas enclosed by the semiautomatically detected contours were slightly larger than the manual ones (bias < 2.1%). The biases in the observer studies were smaller in the semiautomated versus the manual case (0.3% vs. 1.3%). The standard deviation of the MLD differences with a manual analysis was larger by a factor of five than in the semiautomated case. On average, manual analysis required 2 minutes, 18 seconds per lung; this time was reduced to 11.5 to 29 seconds with the semiautomated approach, depending on the respiration state. CONCLUSIONS: The semiautomated approach is preferred over the manual approach because of its higher consistency and its shorter analysis time.