Lung MRI and image processing communityPercent ventilated volume (%VV) is a quantitative measure of lung ventilation derived from hyperpolarised (HP) gas and ¹H lung MRI often used in pulmonary disease assessment for quantitative evaluation of early obstructive changes¹. In order to calculate %VV, both HP gas ventilation images and proton anatomical images must be segmented. Hard clustering methods have previously been used^2,3 for the segmentation of HP gas images with high success rates. Developments in Fuzzy C-means segmentation algorithms have included the use of spatial information in the membership function calculation⁴ for image segmentation providing an alternative to the k-means algorithms commonly used.The aims of this study were, (1) to develop a graphical user interface (GUI) for semi-automatic %VV calculation using a spatial Fuzzy c-means (SFCM) algorithm, and (2) to determine if the GUI would reduce interobserver variability when compared with the existing workflow of manual thresholding and interpolation to calculate %VV.6 patient data sets were analysed after undergoing same-breath hyperpolarised ³He MRI and ¹H MRI at 1.5T⁵ (GE HDx, Milwaukee, WI). Patients were referred for scanning with a variety of pulmonary conditions including asthma, COPD and CF, and ages ranged from 23 to 68 years (3 male, 3 female). A manual method of %VV calculation was compared to a semi-automatic method using the custom-built GUI:To apply the SFCM method (figure 1) images were filtered using a bilateral filter⁶ and segmented using a user-defined number of SFCM clusters. The user then chose the most appropriate cluster representing the lung and this cluster was transformed to a binary mask. Finally airways and any erroneous data were excluded manually using ITK-SNAP⁷, launched from within the GUI.Data was segmented using manual thresholding of ventilation images and manual interpolation of the total lung volume defined from proton images overlaid with ventilation images.Data was analysed by two observers (O1 and O2) using both segmentation techniques to calculate %VV as ventilated volume (from HP ³He images) divided by total lung volume (from ¹H images)⁵. O1 and O2 had 6 and 1 years' experience of lung image segmentation respectively. Correlation, Bland-Altman and Mann-Whitney analyses were carried out.Interobserver correlation improved when using the SFCM method (r=0.99) when compared to using the manual method (r=0.93) (figure 2). Furthermore the amount of bias between observers was reduced when using the SFCM method (bias=1.10±2.87) when compared to the manual method (bias=-6.96±6.55) as seen in figure 3. Intermethod correlation for both observers was also high (r=0.93 for O1 and r=0.87 for O2), although there is a relatively large intermethod bias (bias=3.42±8.46 for O1 and bias=9.28±2.87 for O2). This bias is expected due to the way total lung volume is calculated using information from the ventilation image in the manual method whereas the SFCM uses the anatomical image only, meaning some edge slices containing minimal ventilation are excluded in the SFCM method but not in the manual method (e.g. due to partial-voluming effects). No significant difference was found between the two observers’ SFCM measurements for all patient slices (n=229, p=0.56) whilst there was a significant difference between the manual method (n=229, p < 0.0001). The average time spent segmenting data sets was reduced when using the SFCM method (22±5.5 mins) when compared to the manual method (38±7.5 mins). Furthermore the SFCM method is a user friendly and simple way to segment lung MR images.A GUI using SFCM has been developed and its utility in calculating %VV has been demonstrated, with a significant reduction in interobserver variability and analysis time in comparison with manual thresholding and interpolation.